KAT-Coder-Air V2.5 vs Kimi K2.7 Code: which should you choose in 2026?
KAT-Coder-Air V2.5 (by KwaiPilot) and Kimi K2.7 Code (by Moonshot AI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
KAT-Coder-Air V2.5 is about 5.4× cheaper on a blended 3:1 input/output basis ($0.2625 vs $1.41 per 1M tokens).
Choose KAT-Coder-Air V2.5 if…
- • lower blended API cost matters for your workload
Choose Kimi K2.7 Code if…
- • you need a model with explicit reasoning support
- • you need image inputs
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 | KAT-Coder-Air V2.5 | Kimi K2.7 Code |
|---|---|---|
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 | — | — |
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 | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
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 |
| Arena preference scores | ||
Human preference score for code and web development | — | 1473 ±10 |
Benchmark sources
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 18, 2026.
Pricing, capabilities, and model facts
| Feature | KAT-Coder-Air V2.5 | Kimi K2.7 Code |
|---|---|---|
| Context & model facts | ||
| Developer | KwaiPilot | Moonshot AI |
| API provider | Kwaipilot | MoonshotAI |
| Input context | 256,000 tokens | 262,144 tokens |
| Maximum output | 80,000 tokens | 262,144 tokens |
| Released | — | — |
| Added to Writingmate | Jul 10, 2026 | Jun 12, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text | Text, Image |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | No | Yes |
| Vision | No | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.15 | $0.71 |
| Output (per 1M tokens) | $0.60 | $3.50 |
| Blended 3:1 input/output | $0.2625 | $1.41 |
| 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 Jul 10, 2026.
Why Pay for Multiple Subscriptions?
Comparing KAT-Coder-Air V2.5 from KwaiPilot with Kimi K2.7 Code from Moonshot AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | KAT-Coder-Air V2.5 | Kimi K2.7 Code | 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 |
KAT-Coder-Air V2.5 vs Kimi K2.7 Code FAQ
Which is better, KAT-Coder-Air V2.5 or Kimi K2.7 Code?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. KAT-Coder-Air V2.5 is about 5.4× cheaper on a blended 3:1 input/output basis ($0.2625 vs $1.41 per 1M tokens).
Which model is cheaper to use through an API?
KAT-Coder-Air V2.5 is about 5.4× cheaper on a blended 3:1 input/output basis ($0.2625 vs $1.41 per 1M tokens).
Which model supports more context?
The published context windows are close enough that this page does not treat the difference as a material advantage.
Can I switch between KAT-Coder-Air V2.5 and Kimi K2.7 Code?
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.
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