Kimi K2.7 Code vs GPT-5.2-Codex: which should you choose in 2026?
Kimi K2.7 Code (by Moonshotai) and GPT-5.2-Codex (by OpenAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
Kimi K2.7 Code outperforms in 0 benchmarks, while GPT-5.2-Codex is better at 1 benchmark (LiveBench).
GPT-5.2-Codex leads every shared, protocol-compatible benchmark shown here.
Kimi K2.7 Code is about 3.4× cheaper on a blended 3:1 input/output basis ($1.40 vs $4.81 per 1M tokens).
GPT-5.2-Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Choose Kimi K2.7 Code if…
- • lower blended API cost matters for your workload
Choose GPT-5.2-Codex if…
- • you want the model leading more of the 1 shared benchmark
- • you work with longer documents, transcripts, or codebases
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Kimi K2.7 Code | GPT-5.2-Codex |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | 31.0%±1.0 DataCurve | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | 72.8% SWE-bench |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 68.4% LiveBench | 74.0% LiveBench |
| Arena preference scores | ||
Human preference score for code and web development | 1473 ±10 | 1338 ±9 |
Benchmark sources
- Arena AI leaderboard
- DeepSWE 1.1 Leaderboard
- SWE-bench Verified Bash Only Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 9, 2026.
Pricing, capabilities, and model facts
| Feature | Kimi K2.7 Code | GPT-5.2-Codex |
|---|---|---|
| Context & model facts | ||
| Developer | Moonshotai | OpenAI |
| API provider | MoonshotAI | OpenAI |
| Input context | 262,144 tokens | 400,000 tokens |
| Maximum output | 262,144 tokens | 128,000 tokens |
| Released | — | — |
| Added to Writingmate | Jun 12, 2026 | Jan 14, 2026 |
| License | Modified MIT | Proprietary |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text, Image | Text, Image |
| Outputs | Text | Text |
| Tool use | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.70 | $1.75 |
| Output (per 1M tokens) | $3.50 | $14.00 |
| Blended 3:1 input/output | $1.40 | $4.81 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Jun 12, 2026.
Why Pay for Multiple Subscriptions?
Comparing Kimi K2.7 Code from Moonshotai with GPT-5.2-Codex from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Kimi K2.7 Code | GPT-5.2-Codex | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Kimi K2.7 Code vs GPT-5.2-Codex FAQ
Which is better, Kimi K2.7 Code or GPT-5.2-Codex?
Kimi K2.7 Code outperforms in 0 benchmarks, while GPT-5.2-Codex is better at 1 benchmark (LiveBench). GPT-5.2-Codex leads every shared, protocol-compatible benchmark shown here. Kimi K2.7 Code is about 3.4× cheaper on a blended 3:1 input/output basis ($1.40 vs $4.81 per 1M tokens). GPT-5.2-Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Which model is cheaper to use through an API?
Kimi K2.7 Code is about 3.4× cheaper on a blended 3:1 input/output basis ($1.40 vs $4.81 per 1M tokens).
Which model supports more context?
GPT-5.2-Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Can I switch between Kimi K2.7 Code and GPT-5.2-Codex?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.