Kimi K2.5 vs Llama 3.2 1B
Kimi K2.5 (2026) and Llama 3.2 1B (2024) are agentic coding models from Moonshot AI and AI at Meta. Kimi K2.5 ships a 256K-token context window, while Llama 3.2 1B ships a 128K-token context window. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $0.38/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~283% cheaper at $0.1/1M; pay for Kimi K2.5 only for coding workflow support.
Specs
| Released | 2026-03-15 | 2024-09-25 |
| Context window | 256K | 128K |
| Parameters | 1T (MoE, 384 experts) | 1.23B |
| Architecture | mixture of experts | decoder only |
| License | MIT | Open Source |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Kimi K2.5 | Llama 3.2 1B | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.1/1M tokens |
| Output price | $1.72/1M tokens | $0.1/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Llama 3.2 1B | |
|---|---|---|
| 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 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 Llama 3.2 1B lists $0.1/1M input and $0.1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $0.68 per million blended tokens. Availability is 7 providers versus 1, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Llama 3.2 1B when provider fit and lower input-token cost 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 3.2 1B?
Kimi K2.5 supports 256K tokens, while Llama 3.2 1B 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 3.2 1B?
Llama 3.2 1B is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Llama 3.2 1B costs $0.1/1M input and $0.1/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Llama 3.2 1B open source?
Kimi K2.5 is listed under MIT. Llama 3.2 1B 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 3.2 1B?
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 3.2 1B?
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 Llama 3.2 1B?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Llama 3.2 1B is available on Fireworks AI. 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.