Kimi K2.5 vs Mixtral 8x7B
Kimi K2.5 (2026) and Mixtral 8x7B (2023) are agentic coding models from Moonshot AI and MistralAI. Kimi K2.5 ships a 256K-token context window, while Mixtral 8x7B ships a 32K-token context window. On Google-Proof Q&A, Kimi K2.5 leads by 33.1 pts. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.38/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mixtral 8x7B is ~155% cheaper at $0.15/1M; pay for Kimi K2.5 only for coding workflow support.
Specs
| Released | 2026-03-15 | 2023-12-11 |
| Context window | 256K | 32K |
| Parameters | 1T (MoE, 384 experts) | 8x7B |
| Architecture | mixture of experts | mixture of experts |
| License | MIT | Apache 2.0 |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Kimi K2.5 | Mixtral 8x7B | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.15/1M tokens |
| Output price | $1.72/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Mixtral 8x7B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Kimi K2.5 | Mixtral 8x7B |
|---|---|---|
| Google-Proof Q&A | 87.9 | 54.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Kimi K2.5 at 87.9 and Mixtral 8x7B at 54.8, with Kimi K2.5 ahead by 33.1 points. The largest visible gap is 33.1 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 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.54 per million blended tokens. Availability is 7 providers versus 18, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support and larger context windows are central to the workload. Choose Mixtral 8x7B 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 8x7B?
Kimi K2.5 supports 256K tokens, while Mixtral 8x7B supports 32K 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 8x7B?
Mixtral 8x7B is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Mixtral 8x7B open source?
Kimi K2.5 is listed under MIT. Mixtral 8x7B 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 8x7B?
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 8x7B?
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 8x7B?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI 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.