Kimi K2 Instruct 0905 vs Qwen3-235B-A22B
Kimi K2 Instruct 0905 (2025) and Qwen3-235B-A22B (2025) are compact production models from Moonshot AI and Alibaba. Kimi K2 Instruct 0905 ships a 256K-token context window, while Qwen3-235B-A22B ships a 128K-token context window. On pricing, Qwen3-235B-A22B costs $0.4/1M input tokens versus $0.6/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3-235B-A22B is ~50% cheaper at $0.4/1M; pay for Kimi K2 Instruct 0905 only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Kimi K2 Instruct 0905 | Qwen3-235B-A22B |
|---|---|---|
| Decision fit | Long context | Coding, RAG, and Long context |
| Context window | 256K | 128K |
| Cheapest output | $2.5/1M tokens | $1.2/1M tokens |
| Provider routes | 2 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Kimi K2 Instruct 0905 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Kimi K2 Instruct 0905 for Long context.
- Qwen3-235B-A22B has the lower cheapest tracked output price at $1.2/1M tokens.
- Qwen3-235B-A22B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3-235B-A22B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen3-235B-A22B for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Kimi K2 Instruct 0905
$1,105
Cheapest tracked route: Fireworks AI
Qwen3-235B-A22B
$620
Cheapest tracked route: AWS Bedrock
Estimated monthly gap: $485. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Qwen3-235B-A22B is $1.3/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3-235B-A22B adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Kimi K2 Instruct 0905 is $1.3/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-01-01 |
| Context window | 256K | 128K |
| Parameters | — | 235B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Kimi K2 Instruct 0905 | Qwen3-235B-A22B |
|---|---|---|
| Input price | $0.6/1M tokens | $0.4/1M tokens |
| Output price | $2.5/1M tokens | $1.2/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2 Instruct 0905 | Qwen3-235B-A22B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Qwen3-235B-A22B. 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 Instruct 0905 lists $0.6/1M input and $2.5/1M output tokens, while Qwen3-235B-A22B lists $0.4/1M input and $1.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-235B-A22B lower by about $0.53 per million blended tokens. Availability is 2 providers versus 4, so concentration risk also matters.
Choose Kimi K2 Instruct 0905 when long-context analysis and larger context windows are central to the workload. Choose Qwen3-235B-A22B 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. 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Kimi K2 Instruct 0905 or Qwen3-235B-A22B?
Kimi K2 Instruct 0905 supports 256K tokens, while Qwen3-235B-A22B 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 Instruct 0905 or Qwen3-235B-A22B?
Qwen3-235B-A22B is cheaper on tracked token pricing. Kimi K2 Instruct 0905 costs $0.6/1M input and $2.5/1M output tokens. Qwen3-235B-A22B costs $0.4/1M input and $1.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2 Instruct 0905 or Qwen3-235B-A22B open source?
Kimi K2 Instruct 0905 is listed under Proprietary. Qwen3-235B-A22B 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 structured outputs, Kimi K2 Instruct 0905 or Qwen3-235B-A22B?
Qwen3-235B-A22B 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 Instruct 0905 and Qwen3-235B-A22B?
Kimi K2 Instruct 0905 is available on Fireworks AI and NVIDIA NIM. Qwen3-235B-A22B is available on Fireworks AI, AWS Bedrock, OpenRouter, and Venice AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Kimi K2 Instruct 0905 over Qwen3-235B-A22B?
Qwen3-235B-A22B is ~50% cheaper at $0.4/1M; pay for Kimi K2 Instruct 0905 only for long-context analysis. If your workload also depends on long-context analysis, start with Kimi K2 Instruct 0905; if it depends on provider fit, run the same evaluation with Qwen3-235B-A22B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.