GPT-5.1 Codex vs Kimi K2 Thinking Turbo
GPT-5.1 Codex (2025) and Kimi K2 Thinking Turbo (2025) compare a coding-specialized model against a standalone API model. GPT-5.1 Codex ships a 400k-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 page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GPT-5.1 Codex is coding-specialized model, while Kimi K2 Thinking Turbo is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | GPT-5.1 Codex | Kimi K2 Thinking Turbo |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | general production evaluation |
| Decision fit | Coding, RAG, and Agents | Long context |
| Context window | 400k | 262k |
| Cheapest output | $10/1M tokens | $8/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.1 Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.1 Codex uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags GPT-5.1 Codex for Coding, RAG, and Agents.
- 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.
GPT-5.1 Codex
$3,500
Cheapest tracked route/tier: Vercel AI Gateway
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
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- Kimi K2 Thinking Turbo is $2/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.1 Codex is $2/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GPT-5.1 Codex adds Vision, Multimodal, and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2025-11-06 |
| Context window | 400k | 262k |
| Parameters | — | 1T (32B active) |
| Architecture | decoder only | - |
| License | Proprietary | MIT(OSI) |
| Openness | Proprietary | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2024-09 | - |
Pricing and availability
| Pricing attribute | GPT-5.1 Codex | Kimi K2 Thinking Turbo |
|---|---|---|
| Input price | $1.25/1M tokens | $1.15/1M tokens |
| Output price | $10/1M tokens | $8/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.1 Codex | Kimi K2 Thinking Turbo |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-5.1 Codex, multimodal input: GPT-5.1 Codex, function calling: GPT-5.1 Codex, tool use: GPT-5.1 Codex, structured outputs: GPT-5.1 Codex, and code execution: GPT-5.1 Codex. 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 Codex 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 Codex when coding workflow support and larger context windows 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.
FAQ
Which has a larger context window, GPT-5.1 Codex or Kimi K2 Thinking Turbo?
GPT-5.1 Codex supports 400k 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 Codex or Kimi K2 Thinking Turbo?
Kimi K2 Thinking Turbo is cheaper on tracked token pricing. GPT-5.1 Codex 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 Codex or Kimi K2 Thinking Turbo open source?
GPT-5.1 Codex is listed under Proprietary. Kimi K2 Thinking Turbo is listed under MIT. 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 vision, GPT-5.1 Codex or Kimi K2 Thinking Turbo?
GPT-5.1 Codex has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.1 Codex or Kimi K2 Thinking Turbo?
GPT-5.1 Codex 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.
Where can I run GPT-5.1 Codex and Kimi K2 Thinking Turbo?
GPT-5.1 Codex is available on Vercel AI Gateway. Kimi K2 Thinking Turbo is available on Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.