Kimi K2.5 vs Mixtral 8x22B v0.1
Kimi K2.5 (2026) and Mixtral 8x22B v0.1 (2024) are agentic coding models from Moonshot AI and MistralAI. Kimi K2.5 ships a 256K-token context window, while Mixtral 8x22B v0.1 ships a 64K-token context window. On Google-Proof Q&A, Kimi K2.5 leads by 27.8 pts. On pricing, Mixtral 8x22B v0.1 costs $0.3/1M input tokens versus $0.38/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Kimi K2.5 fits 4x more tokens; pick it for long-context work and Mixtral 8x22B v0.1 for tighter calls.
Specs
| Released | 2026-03-15 | 2024-04-17 |
| Context window | 256K | 64K |
| Parameters | 1T (MoE, 384 experts) | 8x22B |
| Architecture | mixture of experts | mixture of experts |
| License | MIT | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Kimi K2.5 | Mixtral 8x22B v0.1 | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.3/1M tokens |
| Output price | $1.72/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Mixtral 8x22B v0.1 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Kimi K2.5 | Mixtral 8x22B v0.1 |
|---|---|---|
| Google-Proof Q&A | 87.9 | 60.1 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Kimi K2.5 at 87.9 and Mixtral 8x22B v0.1 at 60.1, with Kimi K2.5 ahead by 27.8 points. The largest visible gap is 27.8 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on function calling: Kimi K2.5 and structured outputs: Kimi K2.5. 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, Kimi K2.5 lists $0.38/1M input and $1.72/1M output tokens, while Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x22B v0.1 lower by about $0.3 per million blended tokens. Availability is 7 providers versus 8, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support and larger context windows are central to the workload. Choose Mixtral 8x22B v0.1 when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Kimi K2.5 or Mixtral 8x22B v0.1?
Kimi K2.5 supports 256K tokens, while Mixtral 8x22B v0.1 supports 64K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Kimi K2.5 or Mixtral 8x22B v0.1?
Mixtral 8x22B v0.1 is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Mixtral 8x22B v0.1 costs $0.3/1M input and $0.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Mixtral 8x22B v0.1 open source?
Kimi K2.5 is listed under MIT. Mixtral 8x22B v0.1 is listed under Apache 2.0. 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 function calling, Kimi K2.5 or Mixtral 8x22B v0.1?
Kimi K2.5 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Kimi K2.5 or Mixtral 8x22B v0.1?
Kimi K2.5 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Kimi K2.5 and Mixtral 8x22B v0.1?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.