Claude Opus 5
Claude Opus 5 is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.
Use it for
- Teams evaluating coding, rag, and agents
- Workloads that can use a 1m context window
- Buyers comparing 4 tracked provider routes
Do not use it for
- Workloads where another current model has stronger sourced task evidence
- Family
- Claude 5
- Released
- 2026-07-24
- Context
- 1m
- Max output
- 128,000
- Architecture
- Decoder Only
- Knowledge cutoff
- 2026-05
- Specialization
- general
- Openness
- Proprietary
- License
- ProprietaryCommercial use: conditional
- Weights
- Not released
- Code
- Not released
- Training
- Pretrained
Cheapest of 7 routes · Anthropic · cache read $0.500
About
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.
Claude Opus 5 is a proprietary model in the Claude 5 family. The structured metadata tracks a 1m-token context window, multimodal input, reasoning, function calling, tool use, structured outputs, and code execution. This page tracks provider routes through Anthropic, AWS Bedrock, GCP Vertex AI, and 4 more, with the cheapest tracked route listed at $5 input and $25 output per 1M tokens. Headline tracked benchmarks include SWE-bench Verified 96.0, SWE-bench Pro 79.2, and SWE-bench Multilingual 89.5.
Top use-case fit: coding, agents, and build tasks
Coding
Q/$ D2 relevant benchmarks in the decision map.
RAG
Included by capability and metadata signals in the decision map.
Agents
Q/$ D1 relevant benchmark in the decision map.
Provider price ladder
Compare all 7Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.
| Provider | Input / 1M | Output / 1M | Batch in / out | Cache | Route |
|---|---|---|---|---|---|
| Anthropic | $5.00 | $25.00 | $2.50 / $12.50 | read $0.500 / 5m $6.25 / 1h $10.00 | Serverless |
| GCP Vertex AI | $5.00 | $25.00 | $2.50 / $12.50 | read $0.500 / 5m $6.25 / 1h $10.00 | Serverless |
| OpenRouter | $5.00 | $25.00 | - | - | Serverless |
| Vercel AI Gateway | $5.00 | $25.00 | - | read $0.500 / 5m $6.25 | Serverless |
Available via routers & gateways(16)
LiteLLM
GatewayOpen-source Python SDK and proxy server that unifies 100+ LLM APIs behind a single OpenAI-compatible interface, with load balancing, cost tracking, and configurable failover.
OpenRouter
HybridUnified hybrid gateway to 400+ models from 60+ providers via a single OpenAI-compatible API, with optional auto-routing that selects the best model per prompt.
Portkey
GatewayProduction AI gateway routing to 1,600+ LLMs with failover, load balancing, semantic caching, and guardrails; Apache 2.0 core is fully self-hostable with the complete feature set.
AIRouter
RouterCommercial LLM router that analyzes incoming requests and routes to the optimal model for cost/quality/latency via a drop-in OpenAI-compatible API, with a privacy-preserving embedding mode that avoids sending prompt content.
Amazon Bedrock Intelligent Prompt Routing
RouterAWS Bedrock's native intelligent prompt router that routes prompts between Anthropic Claude model tiers (Haiku/Sonnet) based on predicted task complexity, with no extra per-routing charge.
Azure AI Foundry Model Router
RouterMicrosoft Azure AI Foundry's native model router that uses a trained ML model to route each prompt in real time to the optimal Azure-hosted model, with Balanced/Cost/Quality mode selection and automatic failover.
Capabilities
Benchmark peer barsfor Coding
Benchmark scores(10)
| Benchmark | Score | Version | Evaluation | Source |
|---|---|---|---|---|
| SWE-bench Verified | 96.0 | Verified, 500-problem solvable subsetObserved 2026-07-24 | Configuration: Adaptive thinking at maximum effort Harness: Anthropic system-card standard configuration; average of five trials Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: p.149, Table 8.1.A. Preserve the five-trial setting; do not treat single-trial rows as equivalent. | Source |
| SWE-bench Pro | 79.2 | SWE-bench ProObserved 2026-07-24 | Configuration: Claude Opus 5 standard configuration Harness: Anthropic system-card standard configuration; average of five trials Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: p.149, Table 8.1.A. This is distinct from SWE-bench Verified. | Source |
| SWE-bench Multilingual | 89.5 | SWE-bench MultilingualObserved 2026-07-24 | Configuration: Claude Opus 5 standard configuration Harness: Anthropic system-card standard configuration; average of five trials Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: p.149, Table 8.1.A. This multilingual suite is not the English SWE-bench Verified row. | Source |
| SWE-bench Multimodal | 59.4 | SWE-bench MultimodalObserved 2026-07-24 | Configuration: Claude Opus 5 standard configuration Harness: Anthropic internal SWE-bench Multimodal harness Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: p.149, Table 8.1.A and Section 9.3. Do not compare this internal-harness row as identical to third-party runs. | Source |
| DeepSWE 1.1 | 68.8 | DeepSWE v1.1Observed 2026-07-24 | Configuration: Maximum effort Harness: 113 long-horizon agentic software-engineering tasks; five-trial average Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: pp.149-150, Table 8.1.A and Figure 8.2. The effort sweep ranges from 57.7 at low to 68.8 at maximum. | Source |
| FrontierCode 1.1 Main | 53.4 | FrontierCode 1.1 MainObserved 2026-07-24 | Configuration: Medium effort Harness: 100-task mean@5 composite of held-out-test performance and weighted code quality Evaluator: Cognition Confidence: confirmed Notes: Source: p.150, Figure 8.3. Medium is the reported Opus 5 setting; preserve it and keep Main separate from Extended. | Source |
| FrontierCode 1.1 Extended | 63.6 | FrontierCode 1.1 ExtendedObserved 2026-07-24 | Configuration: Medium effort Harness: Cognition FrontierCode 1.1 weighted code-quality and held-out-test mean@5 evaluation Evaluator: Cognition Confidence: confirmed Notes: Source: p.151, Figure 8.4. Keep Extended separate from FrontierCode Main. | Source |
| Frontier-Bench v0.1 | 44.4 | Frontier-Bench v0.1Observed 2026-07-24 | Configuration: xhigh effort Harness: 74 tasks; mini-SWE-agent on Google Kubernetes Engine; mean reward over five attempts per task Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: p.152, Figure 8.5. About 5% of API calls were refused and 4% of trials fell back to Opus 4.8; maximum effort scored 43.0 within noise of xhigh. | Source |
| ArXivMath June 2026 | 90.8 | June 2026Observed 2026-07-24 | Configuration: Maximum effort, no tools Harness: 49-problem June 2026 set; four runs per problem Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: pp.154-155, Figure 8.10. Store separately from the 91.33 with-tools configuration. | Source |
| ArXivMath June 2026 | 91.3 | June 2026Observed 2026-07-24 | Configuration: Maximum effort, with tools Harness: 49-problem June 2026 set; four runs per problem Evaluator: Anthropic Confidence: confirmed Notes: Vendor-reported. Source: pp.154-155, Figure 8.10. Store separately from the 90.82 no-tools configuration. | Source |
Migration checks
No linked migration route is available for this model yet.
API versions
claude-opus-5Rankings & picks(4)
Comparison and alternatives
Browse all comparisons →Frequently asked questions
What is the context window of Claude Opus 5?
Claude Opus 5 has a context window of 1m tokens.
What is the max output of Claude Opus 5?
Claude Opus 5 can generate up to 128,000 output tokens.
How much does Claude Opus 5 cost?
Claude Opus 5 is available at $5.00/1M input tokens through Anthropic.
When was Claude Opus 5 released?
Claude Opus 5 was released on 2026-07-24.
Which providers offer Claude Opus 5?
Claude Opus 5 is available from 7 providers: Anthropic, AWS Bedrock, GCP Vertex AI, Microsoft Foundry, OpenRouter, Vercel AI Gateway, Databricks Foundation Model Serving.
What benchmarks has Claude Opus 5 been tested on?
Claude Opus 5 has been evaluated on 10 benchmarks, including SWE-bench Verified, SWE-bench Pro, SWE-bench Multilingual, SWE-bench Multimodal, DeepSWE 1.1.
Cheapest of 7 routes · Anthropic · cache read $0.500