Kimi K2 Thinking vs Llama 3.2 90B Instruct
Kimi K2 Thinking (2025) and Llama 3.2 90B Instruct (2025) are frontier reasoning models from Moonshot AI and AI at Meta. Kimi K2 Thinking ships a 256k-token context window, while Llama 3.2 90B Instruct ships a 128k-token context window. On pricing, Kimi K2 Thinking costs $0.60/1M input tokens versus $1.35/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Kimi K2 Thinking is ~125% cheaper at $0.60/1M; pay for Llama 3.2 90B Instruct only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Kimi K2 Thinking | Llama 3.2 90B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | multimodal apps |
| Decision fit | RAG, Long context, and Classification | RAG, Long context, and Vision |
| Context window | 256k | 128k |
| Cheapest output | $2.50/1M tokens | $1.80/1M tokens |
| Provider routes | 7 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Kimi K2 Thinking has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Kimi K2 Thinking has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2 Thinking uniquely exposes Reasoning in local model data.
- Local decision data tags Kimi K2 Thinking for RAG, Long context, and Classification.
- Llama 3.2 90B Instruct has the lower cheapest tracked output price at $1.80/1M tokens.
- Llama 3.2 90B Instruct uniquely exposes Vision and Multimodal in local model data.
- 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 Thinking
$1,105
Cheapest tracked route/tier: Fireworks AI
Llama 3.2 90B Instruct
$1,530
Cheapest tracked route/tier: AWS Bedrock
Estimated monthly gap: $425. 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 $0.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning before moving production traffic.
- Llama 3.2 90B Instruct adds Vision and Multimodal in local capability data.
- Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
- Kimi K2 Thinking is $0.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Kimi K2 Thinking adds Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-09-01 |
| Context window | 256k | 128k |
| Parameters | 1T (32B active) | 90B |
| Architecture | decoder only | - |
| License | MIT(OSI) | Llama 3 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | Kimi K2 Thinking | Llama 3.2 90B Instruct |
|---|---|---|
| Input price | $0.60/1M tokens | $1.35/1M tokens |
| Output price | $2.50/1M tokens | $1.80/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2 Thinking | Llama 3.2 90B Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | No | 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 vision: Llama 3.2 90B Instruct, multimodal input: Llama 3.2 90B Instruct, and reasoning mode: Kimi K2 Thinking. 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 Thinking lists $0.60/1M input and $2.50/1M 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 Thinking lower by about $0.31 per million blended tokens. Availability is 7 providers versus 1, so concentration risk also matters.
Choose Kimi K2 Thinking when reasoning depth, 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.
FAQ
Which has a larger context window, Kimi K2 Thinking or Llama 3.2 90B Instruct?
Kimi K2 Thinking 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 Thinking or Llama 3.2 90B Instruct?
Kimi K2 Thinking is cheaper on tracked token pricing. Kimi K2 Thinking costs $0.60/1M input and $2.50/1M 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 Thinking or Llama 3.2 90B Instruct open source?
Kimi K2 Thinking is listed under MIT. 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 Thinking or Llama 3.2 90B Instruct?
Llama 3.2 90B Instruct has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Kimi K2 Thinking or Llama 3.2 90B Instruct?
Llama 3.2 90B Instruct has the clearer documented multimodal input signal in this comparison. If multimodal input 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 Thinking and Llama 3.2 90B Instruct?
Kimi K2 Thinking is available on Fireworks AI, GCP Vertex AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Llama 3.2 90B Instruct is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.