Context window

RWKV-7 Goose 0.1B (approximately 190M parameters) is the smallest model in the RWKV-7 Goose series. Suitable for ultra-low-resource deployment with constant-memory inference. Uses RWKV-7 architecture with the Generalized Delta Rule. Trained on the World v2.8 corpus. Apache 2.0 licensed.

No tracked provider route

RWKV-6 Finch 1.6B is the smallest model in the RWKV-6 Finch series, ideal for lightweight deployments requiring constant-memory inference. Apache 2.0 licensed.

No tracked provider route

RWKV-7 Goose 0.4B (approximately 450M parameters) is a lightweight model from the RWKV-7 Goose series. Designed for edge deployment and resource-constrained environments where constant-memory O(1) inference is critical. Uses RWKV-7 architecture with the Generalized Delta Rule. Trained on the World v2.9 corpus. Apache 2.0 licensed.

No tracked provider route

RWKV-6 Finch 3B is a mid-range model in the RWKV-6 Finch series, offering a balance between capability and deployment efficiency. Apache 2.0 licensed. Constant-memory inference with no KV cache.

No tracked provider route

RWKV-7 Goose 1.5B is a mid-range model from the RWKV-7 Goose World3 series. Uses the seventh-generation RWKV architecture with Generalized Delta Rule for expressive dynamic state evolution. Trained on 3.1 trillion tokens from the multilingual World v3 corpus. Constant-memory inference with no KV cache. Apache 2.0 licensed.

No tracked provider route

RWKV-6 Finch 7B is a flagship mid-size model from the RWKV-6 architecture series. Introduced alongside the Eagle and Finch paper (arXiv 2404.05892, April 2024). The Finch 14B model was subsequently derived by stacking two Finch 7B weights. Uses multi-headed matrix-valued states for improved language comprehension. Constant-memory inference. Apache 2.0 licensed.

No tracked provider route

RWKV-7 Goose 2.9B is the largest released model in the RWKV-7 Goose World3 series. Built on the seventh-generation RWKV architecture with the Generalized Delta Rule and dynamic state evolution, it achieves competitive benchmark performance against transformer models of equivalent scale. Trained on 3.1 trillion tokens from the World v3 multilingual corpus (100+ languages, BF16). As a pure recurrent architecture, it requires constant O(1) memory during inference (no KV cache) and processes sequences in linear O(n) time. Licensed Apache 2.0.

No tracked provider route

RWKV-6 Finch 14B is the largest model in the RWKV-6 Finch series, created by stacking two 7B Finch models. Released September 3, 2024. Achieves strong performance on MMLU (56.05%), ARC, HellaSwag (57.69%), and Winogrande (74.43%). The RWKV-6 architecture uses matrix-valued states and dynamic data-driven recurrence, improving comprehension and in-context reasoning compared to RWKV-5 (Eagle). Constant-memory O(1) inference with no KV cache. Apache 2.0 licensed.

No tracked provider route

LTM-2-mini is Magic's research prototype supporting a 100 million token context window, announced August 29, 2024. Uses a novel sequence-dimension algorithm approximately 1,000× more memory-efficient than transformer attention at this scale — requiring only a fraction of a single H100's HBM versus 638 H100s for Llama 3.1 405B at the same context length. Not publicly released for API access or self-hosting; Magic stated they were separately training a full LTM-2 model. Specialization: coding/software development. Source: https://magic.dev/blog/100m-token-context-windows

No tracked provider route

Meta's Llama 4 Scout is a 17-billion parameter mixture-of-experts model with 16 expert routing. Optimized for efficient inference on edge and cloud environments with strong multi-turn conversation capabilities. Available on Cloudflare Workers AI.

$0.170 in / $0.220 out / 1M tokens

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LTM-1 (Long-Term Memory 1) is Magic's first model with a 5 million token context window, announced June 6, 2023. Designed to process entire codebases in context for AI-assisted software development. Architecture and parameter count not publicly disclosed. Not available as a public API; Magic used it in an early-access coding product. Source: https://magic.dev/blog/ltm-1

No tracked provider route

MiniMax-01 combines MiniMax-Text-01 and MiniMax-VL-01, pairing a 456B-total-parameter MoE language model with multimodal understanding for long-context text generation and vision-language tasks.

$0.200 in / $1.10 out / 1M tokens

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Gemini 1.5 Pro, created by Google DeepMind, is a state-of-the-art multimodal large language model that significantly advances over its predecessors in processing and analyzing large datasets across various formats like text, images, audio, and video. It features a highly extended context window of up to 2 million tokens, allowing it to maintain coherence over lengthy interactions. With over 200 billion parameters, the model excels in tasks requiring nuanced language processing, coding assistance, and advanced reasoning. Integrated into Google's platforms such as Vertex AI, Gemini 1.5 Pro also emphasizes ethical considerations, ensuring safety and appropriateness in AI deployment.

$1.25 in / $5.00 out / 1M tokens

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Stable Gemini 1.5 Pro release (February variant) optimized for complex reasoning and high-quality multimodal analysis. Supports 2M context for extended document and video processing.

No tracked provider route

Updated Pro experimental variant with refinements to reasoning depth and creative task performance.

No tracked provider route

Experimental Pro variant with enhanced reasoning and multimodal understanding for complex problem-solving tasks.

No tracked provider route

Google's most capable Gemini 3.5 model, announced at Google I/O on May 19, 2026. Features a 2M-token context window, Deep Think reasoning mode, and frontier multimodal capabilities. In limited Vertex AI enterprise preview as of June 2026; general availability expected by end of June 2026 per Google CEO Sundar Pichai. Pricing and full API documentation pending GA release.

No tracked provider route

Premium extended-reasoning GPT-5.4 variant producing smarter and more precise responses. Replacement for o3-deep-research and o4-mini-deep-research. No prompt caching discount.

$30.00 in / $180.00 out / 1M tokens

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OpenAI's fast, low-cost GPT-5.6 model. Luna is the affordable tier in the Sol, Terra, and Luna lineup, optimized for latency-sensitive applications such as summarization, drafting, autocomplete, and routine automation. Generally available July 9, 2026 across ChatGPT, Codex, and the OpenAI API (model ID gpt-5.6-luna). Supports text and image inputs, reasoning, tool use, prompt caching, and Batch API with a 1,050,000-token context window and 128K max output.

$1.00 in / $6.00 out / 1M tokens

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GPT-5.5 Pro is OpenAI's premium extra-compute deployment of GPT-5.5, released April 23, 2026. It uses the same underlying weights as GPT-5.5 standard with additional parallel test-time compute for harder tasks. Supports text and image inputs, reasoning effort control, tool use, structured outputs, code execution, a 1,050,000-token context window, and 128K max output. Key datapack rows: Terminal-Bench 2.1 78.2%, SWE-bench Pro 58.6%, GPQA Diamond 93.6%, ARC-AGI-2 high effort 83.3%, BrowseComp Pro compute 90.1%, and FrontierMath Tier 4 39.6%. Official pricing is $30/M input, $180/M output, $10/M batch input, and $45/M batch output; native cached input discount is not listed.

$30.00 in / $180.00 out / 1M tokens

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OpenAI's balanced mid-tier GPT-5.6 model. Terra delivers performance competitive with GPT-5.5 at about 2x lower cost, suited for high-volume production workloads such as customer support, document analysis, and internal tooling. Generally available July 9, 2026 across ChatGPT, Codex, and the OpenAI API (model ID gpt-5.6-terra). Supports text and image inputs, reasoning, tool use, prompt caching, and Batch API with a 1,050,000-token context window and 128K max output.

$2.50 in / $15.00 out / 1M tokens

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GPT-5.5 is OpenAI's fully retrained agentic model, released April 23, 2026. Optimised for agentic coding, computer use, knowledge work, and early scientific research. Achieves 82.7% on Terminal-Bench 2.0 (Codex CLI scaffold), 84.9% on GDPval, 58.6% on SWE-Bench Pro, 93.6% on GPQA Diamond, and 82.6% on SWE-Bench Verified (Vals.ai independent harness). Knowledge cutoff December 2025. Supports reasoning effort levels (none/low/medium/high/xhigh). Context window 1,050,000 tokens with a long-context surcharge above 272K tokens. Model ID: gpt-5.5.

$5.00 in / $30.00 out / 1M tokens

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GPT-5.4 is OpenAI's flagship frontier reasoning model, released March 5, 2026. It incorporates advances from GPT-5.3-Codex for coding and agentic workflows, and adds 'Thinking' mode with editable reasoning plans. Key capabilities include computer use (navigating interfaces via Playwright), image understanding and generation integration, full-stack web app generation, tool calling, and deep research. Knowledge cutoff is August 31, 2025. Model ID: gpt-5.4.

$2.50 in / $15.00 out / 1M tokens

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OpenAI's flagship GPT-5.6 model and highest-capability tier in the Sol, Terra, and Luna naming system. GPT-5.6 Sol is built for demanding reasoning, long-horizon coding, agentic workflows, and cybersecurity tasks, introducing max reasoning effort and ultra multi-agent mode. Generally available July 9, 2026 across ChatGPT, Codex, and the OpenAI API (model IDs gpt-5.6-sol and alias gpt-5.6). Supports text and image inputs, reasoning, tool use, code execution, prompt caching, and Batch API with a 1,050,000-token context window and 128K max output.

$5.00 in / $30.00 out / 1M tokens

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Xiaomi MiMo-V2.5 is the lower-cost native omnimodal sibling in the MiMo-V2.5 series. OpenRouter describes it as supporting text, image, audio, and video inputs with text output, Pro-level agentic performance at roughly half the inference cost, and improved multimodal perception over MiMo-V2-Omni. Xiaomi's official April 22 release page highlights MiMo-V2.5 alongside MiMo-V2.5-Pro in benchmark data and says the V2.5 series will be open-sourced soon; no public weights/license were verified at research time.

$0.140 in / $0.280 out / 1M tokens

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Xiaomi's April 22, 2026 public-beta flagship in the MiMo-V2.5 series. The official Xiaomi MiMo page describes MiMo-V2.5-Pro as its most capable model to date, focused on general agentic capability, complex software engineering, long-horizon tasks, and ultra-long-context instruction following. OpenRouter lists it as text-to-text with 1,048,576 token context, 131,072 max completion tokens, reasoning controls, tool use, and response_format support. Xiaomi says the V2.5 series will be open-sourced soon, but no public weights/license were verified at research time.

$0.435 in / $0.870 out / 1M tokens

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Kimi K3 is Moonshot AI's 2.8-trillion-parameter flagship multimodal model for long-horizon coding, knowledge work, deep reasoning, and agentic workflows. It uses Kimi Delta Attention, Attention Residuals, and a sparse MoE design (16 of 896 experts active), supports a 1,048,576-token context window, text/image/video input, always-on reasoning, ToolCalls, strict JSON Schema structured output, automatic context caching, and partial mode through Moonshot's OpenAI-compatible API.

$3.00 in / $15.00 out / 1M tokens

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NVIDIA Nemotron 3 Super-120B-A12B is a 120B total / 12B active hybrid Latent MoE model with interleaved Mamba-2 and MoE layers for agentic, reasoning, and conversational tasks. Fireworks lists the NVFP4 variant for on-demand deployment with 262k context.

$0.090 in / $0.450 out / 1M tokens

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30B MoE model with 3B active parameters - superior reasoning with IMO/IOI 2025 gold-medal performance

No tracked provider route

Gemini 3.5 Flash is Google DeepMind's generally available Flash model for sustained frontier-level performance on agentic and coding tasks. It supports multimodal inputs, native thinking, tool and function calling, structured outputs, code execution, search grounding, batch processing, and long contexts up to 1M tokens.

$1.50 in / $9.00 out / 1M tokens

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Gemini 3.1 Flash-Lite is Google's generally available low-latency Gemini 3.1 model, launched May 7, 2026. It is optimized for high-volume, cost-sensitive workloads with text, image, and video inputs, a 1M token context window, and a 66K token maximum output. The GA model uses the stable API ID gemini-3.1-flash-lite and replaces gemini-3.1-flash-lite-preview, which is scheduled to shut down on May 25, 2026. Pricing is $0.25 per 1M input tokens and $1.50 per 1M output tokens.

$0.250 in / $1.50 out / 1M tokens

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Antigravity Agent is Google DeepMind's preview managed agent for autonomous coding and browsing workflows. Powered by Gemini 3.5 Flash, it plans, reasons, runs code, manages files, and browses the web inside a secure Google-hosted Linux sandbox through the Interactions API. It accepts text and image input, has a 1,048,576-token input context window that compacts at about 135K tokens, and supports a 65,536-token output limit. Environment compute is not billed during preview; Google describes pricing as pay-as-you-go based on underlying Gemini model tokens and tool use.

Pricing not tracked / 1M tokens

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Xiaomi MiMo-V2-Pro language model. The larger, higher-capability model in the MiMo V2 series with an extended 1M token context window.

$1.00 in / $3.00 out / 1M tokens

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Specialized for browser control agents. $1.25/$10.00 (<=200K), $2.50/$15.00 (>200K). Available on AI Studio and Vertex AI; no free tier.

$1.25 in / $10.00 out / 1M tokens

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Gemini 3.5 Flash-Lite is Google DeepMind's generally available multimodal model in the Gemini 3.5 family. It accepts text, image, video, audio, and PDF inputs and returns text, with a 1,048,576-token context window, up to 65,536 output tokens, and Gemini API support for code execution, prompt caching, and batch processing. Compare it for Coding, Agents, Long context, Vision, and JSON / Tool use. In Google AI Studio, select Gemini 3.5 Flash-Lite when lower token cost and high throughput are the priority; select Gemini 3.6 Flash when higher capability matters more.

$0.300 in / $2.50 out / 1M tokens

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Gemini 3.6 Flash is Google DeepMind's generally available multimodal model. It accepts text, image, video, audio, and PDF inputs and returns text, with a 1,048,576-token context window, up to 65,536 output tokens, and Gemini API support for code execution, prompt caching, and batch processing. Compare it for Coding, Agents, Long context, Vision, and JSON / Tool use. In Google AI Studio, select Gemini 3.6 Flash when higher capability is the priority; select Gemini 3.5 Flash-Lite when lower token cost and high throughput matter more.

$1.50 in / $7.50 out / 1M tokens

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Llama 3 70B Gradient 1048K is Gradient's Gradient Llama 3 model. It offers a 1048K-token context window.

No tracked provider route

Llama 3 8B Gradient 1048K is Gradient's Gradient Llama 3 model. It offers a 1048K-token context window.

No tracked provider route

Llama 3.1 8B Gradient 1048K is Gradient's Gradient Llama 3 model. It offers a 1048K-token context window.

No tracked provider route

OpenAI's GPT-4.1 model released April 2025, excelling at coding tasks, precise instruction following, and web development. Outperforms GPT-4o in these areas with a 1 million token context window. Available via API and in ChatGPT for Plus, Pro, Team, Enterprise, and Edu users.

$2.00 in / $8.00 out / 1M tokens

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Fast and efficient small model from OpenAI replacing GPT-4o mini. Released April 2025 alongside GPT-4.1. Shows improvements in instruction-following, coding, and intelligence with a 1 million token context window. Available in ChatGPT for paid users.

$0.400 in / $1.60 out / 1M tokens

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GLM-5.2 is Z.ai's coding-first successor to GLM-5.1 in the GLM-5 family, released June 13 2026. 753B parameters (40B active) in IndexShare MoE architecture; the IndexShare innovation reuses the same attention indexer across every four sparse layers, cutting per-token FLOPs by 2.9x at 1M context length. Trained on 28.5T tokens. Supports a 1M-token context window via the glm-5.2[1m] model ID, with 131,072-token maximum output and High/Max thinking-effort levels designed for extended agentic coding sessions. MIT license; open weights available on Hugging Face (zai-org/GLM-5.2 and zai-org/GLM-5.2-FP8). Self-reported HF card benchmarks: SWE-bench Pro 62.1, Terminal-Bench 2.1 82.7, MCP-Atlas 76.8, Tool-Decathlon 48.2, GPQA Diamond 91.2, AIME 2026 99.2, HLE 40.5. Available to GLM Coding Plan subscribers (Lite/Pro/Max/Team) directly, and via OpenRouter token API ($1.40/$4.40 per 1M tokens).

$1.40 in / $4.40 out / 1M tokens

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Amazon Nova Premier is Amazon's most capable standard Bedrock Nova understanding model for complex reasoning, agentic workflows, and model distillation. It supports a 1M-token context window, text/image/video inputs, text output, reasoning, tool calling, and prompt caching; use it as the standard Bedrock Nova frontier pick instead of Nova 2 Omni early-access Forge checkpoints.

$2.50 in / $12.50 out / 1M tokens

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DeepSeek V4 Pro is DeepSeek's flagship open-weights model, released April 24 2026 under the MIT license. Architecture: 1.6T total / 49B active parameters, MoE with Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) hybrid — requiring only 27% of inference FLOPs vs standard 1M-context transformers — plus Manifold-Constrained Hyper-Connections (mHC) and Muon Optimizer. Context window: 1,000,000 tokens; max output: 384,000 tokens (Think Max mode requires >=384K context). Text-only (no vision/image input). Supports three reasoning modes: Non-Think, Think High, Think Max. Function calling, tool use, and structured outputs supported. Key benchmarks: SWE-bench Verified 80.6%, SWE-bench Pro 55.4%, LiveCodeBench 93.5%, GPQA Diamond 90.1%, MMLU-Pro 87.5%, Terminal-Bench 2.0 59.1% on BenchLM's independent June 2026 harness, and Chatbot Arena 1456 (2026-06-16). Current API pricing: $0.435/$0.87 per 1M input/output tokens; DeepSeek made the former 75% promotional rate permanent in May 2026.

$0.435 in / $0.870 out / 1M tokens

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Alibaba's closed-weight flagship language model, announced at the 2026 Alibaba Cloud Summit (May 20). Scored 56.6 on Artificial Analysis Intelligence Index at launch—highest-ranked Chinese model. 1M-token context with prompt caching (up to 90% discount). Pricing: $2.50/$7.50 per 1M tokens in/out.

$1.25 in / $3.75 out / 1M tokens

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Qwen3.6-Plus is Alibaba Cloud's GA Qwen3.6 flagship for long-context reasoning, coding, tool use, and multimodal workflows. DashScope lists it with a 1M-token context window, structured output support, and standard public token pricing.

$0.325 in / $1.95 out / 1M tokens

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Gemini 1.5 Flash is a large language AI model by Google, crafted for speed and efficiency in high-volume scenarios 145. As a lightweight model, it's optimized for fast processing and cost-effectiveness, making it ideal for real-time applications and high-frequency tasks 567. With its multimodal capabilities, Gemini 1.5 Flash effectively processes and reasons across multiple data types, including text, images, audio, video, and PDFs 145. Despite its smaller size compared to Gemini 1.5 Pro, it excels in tasks like summarization, chat applications, and data extraction from lengthy documents, employing "knowledge distillation" to transfer essential knowledge from larger models 5. Additionally, it features an extensive context window of up to 1 million tokens, allowing it to manage large information volumes effectively 456.

$0.075 in / $0.300 out / 1M tokens

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NVIDIA's open frontier-reasoning model (550B total / 55B active MoE, hybrid Transformer-Mamba). Highest Artificial Analysis Intelligence Index for any US open model (score: 48). 300+ tokens/second. 1M-token context. Announced at Computex 2026. Pricing: ~$0.60/$2.60 per 1M tokens (provider median); free tier on some providers.

$0.500 in / $2.20 out / 1M tokens

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Lightweight 8B variant of Gemini 1.5 Flash optimized for speed and cost-efficiency. Supports 1M token context with fast inference for real-time applications.

$0.0375 in / $0.150 out / 1M tokens

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Gemini 1.5 Flash on Google Vertex AI is Google DeepMind's Gemini 1.5 model with multimodal text and image input. It offers a 1M-token context window.

$0.035 in / $0.105 out / 1M tokens

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Gemini 1.5 Flash on Google Vertex AI (Extended Context) is Google DeepMind's Gemini 1.5 model with multimodal text and image input. It offers a 1M-token context window.

$0.070 in / $0.210 out / 1M tokens

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Gemini 1.5 Pro on Google Vertex AI is Google DeepMind's Gemini 1.5 model with multimodal text and image input. It offers a 1M-token context window.

$0.125 in / $0.375 out / 1M tokens

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Gemini 1.5 Pro on Google Vertex AI (Extended Context) is Google DeepMind's Gemini 1.5 model with multimodal text and image input. It offers a 1M-token context window.

$0.250 in / $0.750 out / 1M tokens

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Google's Gemini 1.0 Ultra is a leading large language model designed for tackling highly complex tasks with advanced analytical capabilities. As the largest model in the Gemini 1.0 family, it excels in coding, mathematical reasoning, and multimodal reasoning. Its strength lies in its ability to seamlessly understand and process diverse data types, including text, code, audio, images, and video. Gemini Ultra surpasses human experts on the MMLU benchmark with a 90% score, although it has limitations in image generation and some multimodal tasks. The model features a 32,000-token context window, less than some competitors, and access is primarily through a paid subscription or via Google Cloud for developers.

$1.00 in / $3.00 out / 1M tokens

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Gemini 2.0 Flash Lite Preview (02-05). Retiring June 1, 2026. Migrate to Gemini 2.5 or Gemini 3 series.

No tracked provider route

Gemini 2.0 Pro (Experimental 02-05) is Google DeepMind's Gemini 2.0 model. Its knowledge cutoff is 2024-08.

No tracked provider route

Google Gemini 2.0 Flash experimental model with 1M context for long-form understanding.

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Google LearnLM experimental model optimized for educational and tutoring applications.

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Stable Gemini 1.5 Flash release (February variant) optimized for high-speed processing and cost efficiency. Supports 1M context with fast token generation for real-time use.

No tracked provider route

Updated experimental 8B Flash with improvements to latency and multimodal understanding capabilities.

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Experimental 8B Flash variant with optimizations for edge deployment and ultra-fast multimodal inference.

No tracked provider route

Experimental Flash variant with enhancements to multimodal capabilities and inference speed.

No tracked provider route

Gemini 3 Flash is Google's speed-optimized Gemini 3 model, available in public preview via the Gemini API and Vertex AI. It supports text, image, audio, and video inputs with a 1M token context window and is priced at $0.50 per 1M input tokens and $3.00 per 1M output tokens.

$0.500 in / $3.00 out / 1M tokens

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Google DeepMind's most advanced reasoning Gemini model. Part of the Gemini 3 series with frontier-class intelligence, multimodal understanding, and 1M token context window.

$1.25 in / $5.00 out / 1M tokens

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Frontier-class performance rivaling larger models at a fraction of the cost. Most intelligent Gemini model built for speed, combining frontier intelligence with superior search and grounding. $0.50 input / $3.00 output per 1M tokens.

$0.500 in / $3.00 out / 1M tokens

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Amazon Nova 2 Pro is a preview text-generation model in the Amazon Nova 2 family, announced for Amazon Bedrock and made available to Amazon Nova Forge customers. It targets higher-capability general reasoning and generation workloads than Nova 2 Lite, supports a 1M-token context window, and currently has restricted preview access rather than standard public token pricing.

Pricing not tracked / 1M tokens

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Amazon Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that processes text, images, and videos at 1M token context with improved reasoning over Nova Lite v1.

$0.300 in / $2.50 out / 1M tokens

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Meta's Llama 4 Maverick 17B with 128 experts, FP8-optimized for cost-efficient inference. Supports native Model Router integration on Microsoft Foundry.

$0.150 in / $0.600 out / 1M tokens

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Alibaba's multimodal agentic model with text, image, and video input. Combines vision-language understanding with full agentic capabilities: deep reasoning, self-programming, tool invocation, and autonomous iteration. GUI grounding: 79.0 on ScreenSpot Pro. Max output 66K tokens. Pricing: $0.40/$1.60 per 1M tokens in/out.

$0.320 in / $1.28 out / 1M tokens

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Anthropic's cybersecurity-focused frontier model, offered as an invitation-only research preview under Project Glasswing. Succeeded by Claude Mythos 5 (API ID: claude-mythos-5) as of June 9, 2026. Anthropic has indicated that Claude Mythos Preview will be retired after Claude Mythos 5 becomes available; no formal retirement date was published as of 2026-06-09. For current access and the migration path, see the Anthropic migration guide.

$25.00 in / $125.00 out / 1M tokens

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Writer lists Palmyra X5 as an active AI Studio API model with text and image input, a 1M-token context window, and an 8,192-token output limit. Its documented capabilities map to Agents, Long context, Vision, and JSON / Tool use.

$0.600 in / $6.00 out / 1M tokens

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Claude Sonnet 4.6 is Anthropic's best combination of speed and intelligence. Proprietary decoder-only model with 1M-token context, 64K max output, multimodal vision, extended thinking, and function calling. Available via Anthropic API, AWS Bedrock, GCP Vertex AI, and OpenRouter at $3/1M input and $15/1M output tokens.

$3.00 in / $15.00 out / 1M tokens

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Claude Opus 4.7 is Anthropic's generally available flagship model with 1M context, 128K max output, adaptive thinking, and a new tokenizer with roughly 555K words per 1M tokens.

$5.00 in / $25.00 out / 1M tokens

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Claude Opus 4.6 is Anthropic's Claude 4.6 model with multimodal text and image input and an optional reasoning mode. It offers a 1M-token context window and scores 80.8 on SWE-bench Verified.

$5.00 in / $25.00 out / 1M tokens

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Claude Opus 4.8 is Anthropic's flagship Claude 4.8 model, released May 28, 2026 for agentic coding, long-horizon reasoning, computer use, and professional knowledge work. It supports text and image inputs, adaptive reasoning, tool use, structured outputs, computer-use tools, prompt caching, Batch API, Dynamic Workflows parallel subagents, a 1M-token context window on Anthropic API/Bedrock/Vertex, and 128K max output. Key datapack rows: SWE-bench Pro 69.2%, SWE-bench Verified 88.6%, Terminal-Bench 2.1 74.6%, HLE with tools 57.9%, OSWorld-Verified 83.4%, GDPval-AA 1890 Elo, and MCP-Atlas 82.2%. Standard Anthropic API pricing is $5/M input and $25/M output.

$5.00 in / $25.00 out / 1M tokens

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Claude Sonnet 5 is Anthropic's next-generation Sonnet model for agentic coding, tool use, computer use, and professional work. It is a proprietary decoder-only model with a 1M-token context window, 128K max output, multimodal vision, adaptive thinking, function calling, structured outputs, prompt caching, and Batch API support. It is available through the Claude API, AWS Bedrock, Google Cloud Vertex AI, Microsoft Foundry preview, and OpenRouter. Anthropic lists durable standard pricing at $3/1M input and $15/1M output tokens, with introductory $2/$10 pricing through 2026-08-31.

$2.00 in / $10.00 out / 1M tokens

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Claude Opus 5 is Anthropic's July 2026 flagship Opus model for complex agentic coding, enterprise work, long-horizon reasoning, computer use, and professional analysis. It accepts text and image input and returns text, with adaptive thinking enabled by default and five request-level effort settings from low through max that control thinking depth and token use rather than visible response length. It also supports tool use, structured outputs, prompt caching, Batch API, a 1M-token context window, and up to 128K synchronous output tokens. Anthropic lists standard Claude API pricing at $5 per million input tokens and $25 per million output tokens, with Fast mode at $10/$50. It is available through the Claude API and hosted provider routes.

$5.00 in / $25.00 out / 1M tokens

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DeepSeek V4 Flash is a 284B parameter (13B activated) Mixture-of-Experts language model with 1M-token context. Features a hybrid attention architecture combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) for efficient long-context inference. Supports thinking and non-thinking modes. Legacy API aliases deepseek-chat and deepseek-reasoner map to this model's non-thinking and thinking modes respectively. Pricing: $0.14/1M input, $0.28/1M output (cache hit: $0.0028/1M input). MIT licensed.

$0.090 in / $0.180 out / 1M tokens

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Google: Gemini 2.5 Flash available via OpenRouter. Pricing: $0.3/1M input, $2.5/1M output.

$0.300 in / $2.50 out / 1M tokens

6 routes · 1 cache

Google: Gemini 3.1 Pro Preview available via OpenRouter. Pricing: $2/1M input, $12/1M output.

$2.00 in / $12.00 out / 1M tokens

5 routes · 1 cache