LLM Reference

GPT-5.1 vs Kimi K2 Thinking Turbo

GPT-5.1 (2025) and Kimi K2 Thinking Turbo (2025) are frontier reasoning models from OpenAI and Moonshot AI. GPT-5.1 ships a 262K-token context window, while Kimi K2 Thinking Turbo ships a 262K-token context window. On pricing, Kimi K2 Thinking Turbo costs $1.15/1M input tokens versus $1.25/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

GPT-5.1 is safer overall; choose Kimi K2 Thinking Turbo when provider fit matters.

Decision scorecard

Local evidence first
SignalGPT-5.1Kimi K2 Thinking Turbo
Best forreasoning-heavy apps, multimodal apps, and tool-calling agentsgeneral production evaluation
Decision fitRAG, Agents, and Long contextLong context
Context window262K262K
Cheapest output$10/1M tokens$8/1M tokens
Provider routes1 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-5.1 when...
  • GPT-5.1 uniquely exposes Multimodal, Reasoning, and Function calling in local model data.
  • Local decision data tags GPT-5.1 for RAG, Agents, and Long context.
Choose Kimi K2 Thinking Turbo when...
  • Kimi K2 Thinking Turbo has the lower cheapest tracked output price at $8/1M tokens.
  • Local decision data tags Kimi K2 Thinking Turbo for Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Lower estimate Kimi K2 Thinking Turbo

GPT-5.1

$3,500

Cheapest tracked route/tier: OpenRouter

Kimi K2 Thinking Turbo

$2,920

Cheapest tracked route/tier: Vercel AI Gateway

Estimated monthly gap: $580. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

GPT-5.1 -> Kimi K2 Thinking Turbo
  • No overlapping tracked provider route is sourced for GPT-5.1 and Kimi K2 Thinking Turbo; plan for SDK, billing, or endpoint changes.
  • Kimi K2 Thinking Turbo is $2/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Multimodal, Reasoning, and Function calling before moving production traffic.
Kimi K2 Thinking Turbo -> GPT-5.1
  • No overlapping tracked provider route is sourced for Kimi K2 Thinking Turbo and GPT-5.1; plan for SDK, billing, or endpoint changes.
  • GPT-5.1 is $2/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • GPT-5.1 adds Multimodal, Reasoning, and Function calling in local capability data.

Specs

Specification
Released2025-11-132025-11-06
Context window262K262K
Parameters1T (32B active)
Architecture--
LicenseProprietaryProprietary
Knowledge cutoff--

Pricing and availability

Pricing attributeGPT-5.1Kimi K2 Thinking Turbo
Input price$1.25/1M tokens$1.15/1M tokens
Output price$10/1M tokens$8/1M tokens
Providers

Capabilities

CapabilityGPT-5.1Kimi K2 Thinking Turbo
VisionNoNo
MultimodalYesNo
ReasoningYesNo
Function callingYesNo
Tool useYesNo
Structured outputsYesNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on multimodal input: GPT-5.1, reasoning mode: GPT-5.1, function calling: GPT-5.1, tool use: GPT-5.1, and structured outputs: GPT-5.1. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, GPT-5.1 lists $1.25/1M input and $10/1M output tokens on the cheapest tracked provider, while Kimi K2 Thinking Turbo lists $1.15/1M input and $8/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2 Thinking Turbo lower by about $0.67 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.

Choose GPT-5.1 when reasoning depth are central to the workload. Choose Kimi K2 Thinking Turbo when provider fit and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, GPT-5.1 or Kimi K2 Thinking Turbo?

GPT-5.1 supports 262K tokens, while Kimi K2 Thinking Turbo supports 262K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, GPT-5.1 or Kimi K2 Thinking Turbo?

Kimi K2 Thinking Turbo is cheaper on tracked token pricing. GPT-5.1 costs $1.25/1M input and $10/1M output tokens. Kimi K2 Thinking Turbo costs $1.15/1M input and $8/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-5.1 or Kimi K2 Thinking Turbo open source?

GPT-5.1 is listed under Proprietary. Kimi K2 Thinking Turbo is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for multimodal input, GPT-5.1 or Kimi K2 Thinking Turbo?

GPT-5.1 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for reasoning mode, GPT-5.1 or Kimi K2 Thinking Turbo?

GPT-5.1 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-5.1 and Kimi K2 Thinking Turbo?

GPT-5.1 is available on OpenRouter. Kimi K2 Thinking Turbo is available on Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Continue comparing

Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.