Kimi K2.5 vs Llama 3.1 70B Instruct
Kimi K2.5 (2026) and Llama 3.1 70B Instruct (2024) are agentic coding models from Moonshot AI and AI at Meta. Kimi K2.5 ships a 256K-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Kimi K2.5 is safer overall; choose Llama 3.1 70B Instruct when provider fit matters.
Specs
| Released | 2026-03-15 | 2024-07-23 |
| Context window | 256K | 128K |
| Parameters | 1T (MoE, 384 experts) | 70B |
| Architecture | mixture of experts | decoder only |
| License | MIT | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Kimi K2.5 | Llama 3.1 70B Instruct | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.4/1M tokens |
| Output price | $1.72/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Llama 3.1 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Kimi K2.5. Both models share structured outputs, 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 Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $0.38 per million blended tokens. Availability is 7 providers versus 11, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Llama 3.1 70B Instruct when provider fit 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Kimi K2.5 or Llama 3.1 70B Instruct?
Kimi K2.5 supports 256K tokens, while Llama 3.1 70B Instruct supports 128K 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 Llama 3.1 70B Instruct?
Kimi K2.5 is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Llama 3.1 70B Instruct open source?
Kimi K2.5 is listed under MIT. Llama 3.1 70B Instruct is listed under Open Source. 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 Llama 3.1 70B Instruct?
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 Llama 3.1 70B Instruct?
Both Kimi K2.5 and Llama 3.1 70B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Kimi K2.5 and Llama 3.1 70B Instruct?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. 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.