Kimi K2.5 vs Llama 3.2 90B Instruct
Kimi K2.5 (2026) and Llama 3.2 90B Instruct (2025) compare a coding-specialized model against a standalone API model. Kimi K2.5 ships a 256k-token context window, while Llama 3.2 90B Instruct ships a 128k-token context window. On pricing, Kimi K2.5 costs $0.44/1M input tokens versus $1.35/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: Kimi K2.5 is coding-specialized model, while Llama 3.2 90B Instruct is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Kimi K2.5 | Llama 3.2 90B Instruct |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | multimodal apps |
| Decision fit | Coding, RAG, and Agents | RAG, Long context, and Vision |
| Context window | 256k | 128k |
| Cheapest output | $2/1M tokens | $1.80/1M tokens |
| Provider routes | 10 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Kimi K2.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Kimi K2.5 has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.5 uniquely exposes Function calling in local model data.
- Local decision data tags Kimi K2.5 for Coding, RAG, and Agents.
- Llama 3.2 90B Instruct has the lower cheapest tracked output price at $1.80/1M tokens.
- Local decision data tags Llama 3.2 90B Instruct for RAG, Long context, and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Kimi K2.5
$852
Cheapest tracked route/tier: OpenRouter
Llama 3.2 90B Instruct
$1,530
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $678. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
- Llama 3.2 90B Instruct is $0.20/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Function calling before moving production traffic.
- Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
- Kimi K2.5 is $0.20/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Kimi K2.5 adds Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-15 | 2025-09-01 |
| Context window | 256k | 128k |
| Parameters | 1T (MoE, 384 experts) | 90B |
| Architecture | mixture of experts | - |
| License | Proprietary | Llama 3 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | Kimi K2.5 | Llama 3.2 90B Instruct |
|---|---|---|
| Input price | $0.44/1M tokens | $1.35/1M tokens |
| Output price | $2/1M tokens | $1.80/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2.5 | Llama 3.2 90B Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
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 vision, multimodal input, and 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 $0.44/1M input and $2/1M output tokens on the cheapest tracked provider, while Llama 3.2 90B Instruct lists $1.35/1M input and $1.80/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $0.58 per million blended tokens. Availability is 10 providers versus 1, 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 3.2 90B Instruct when vision-heavy evaluation 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 90B Instruct?
Kimi K2.5 supports 256k tokens, while Llama 3.2 90B 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 3.2 90B Instruct?
Kimi K2.5 is cheaper on tracked token pricing. Kimi K2.5 costs $0.44/1M input and $2/1M output tokens. Llama 3.2 90B Instruct costs $1.35/1M input and $1.80/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Llama 3.2 90B Instruct open source?
Kimi K2.5 is listed under Proprietary. Llama 3.2 90B Instruct is listed under Llama 3 Community. 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 vision, Kimi K2.5 or Llama 3.2 90B Instruct?
Both Kimi K2.5 and Llama 3.2 90B Instruct expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, Kimi K2.5 or Llama 3.2 90B Instruct?
Both Kimi K2.5 and Llama 3.2 90B Instruct expose multimodal input. 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 3.2 90B Instruct?
Kimi K2.5 is available on Cloudflare Workers AI, Fireworks AI, OpenRouter, Together AI, and NVIDIA NIM. Llama 3.2 90B Instruct is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.