Kimi K2.6 vs Qwen3.7-Max
Kimi K2.6 (2026) and Qwen3.7-Max (2026) compare a coding-specialized model against a standalone API model. Kimi K2.6 ships a 262k-token context window, while Qwen3.7-Max ships a 1m-token context window. On MMLU PRO, Qwen3.7-Max leads by 5 pts. On pricing, Kimi K2.6 costs $0.73/1M input tokens versus $1.25/1M 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 K2.6 is coding-specialized model, while Qwen3.7-Max 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 K2.6 | Qwen3.7-Max |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | reasoning-heavy apps, tool-calling agents, and long-context analysis |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 262k | 1m |
| Cheapest output | $3.49/1M tokens | $3.75/1M tokens |
| Provider routes | 9 tracked | 4 tracked |
| Shared benchmarks | 9 shared | MMLU PRO leader |
Decision tradeoffs
- Kimi K2.6 has the lower cheapest tracked output price at $3.49/1M tokens.
- Kimi K2.6 has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.6 uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Kimi K2.6 for Coding, RAG, and Agents.
- Qwen3.7-Max holds a shared-benchmark lead on MMLU PRO, ahead by 5 points.
- Qwen3.7-Max has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.7-Max uniquely exposes Code execution in local model data.
- Local decision data tags Qwen3.7-Max for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Kimi K2.6
$1,457
Cheapest tracked route/tier: OpenRouter
Qwen3.7-Max
$1,938
Cheapest tracked route/tier: Novita AI
Estimated monthly gap: $481. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway, Novita AI, and OpenRouter; start route-level A/B tests there.
- Qwen3.7-Max is $0.26/1M 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.
- Qwen3.7-Max adds Code execution in local capability data.
- Provider overlap exists on OpenRouter, Vercel AI Gateway, and Novita AI; start route-level A/B tests there.
- Kimi K2.6 is $0.26/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Code execution before moving production traffic.
- Kimi K2.6 adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-20 | 2026-05-19 |
| Context window | 262k | 1m |
| Parameters | 1T | — |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Proprietary |
| Openness | Open source | Proprietary |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | 2025-04 | - |
Pricing and availability
| Pricing attribute | Kimi K2.6 | Qwen3.7-Max |
|---|---|---|
| Input price | $0.73/1M tokens | $1.25/1M tokens |
| Output price | $3.49/1M tokens | $3.75/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2.6 | Qwen3.7-Max |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Kimi K2.6 | Qwen3.7-Max |
|---|---|---|
| MMLU PRO | 84.6 | 89.6 |
| SWE-bench Verified | 80.2 | 80.4 |
| SWE-bench Pro | 58.6 | 60.6 |
| Google-Proof Q&A | 90.5 | 92.4 |
| LiveCodeBench | 89.6 | 91.6 |
| Humanity's Last Exam | 34.7 | 41.4 |
| MCP-Atlas | 55.9 | 76.4 |
| Chatbot Arena | 1462.0 | 1475.0 |
| Terminal-Bench 2.0 | 66.7 | 69.7 |
Deep dive
On shared benchmark coverage, MMLU PRO has Kimi K2.6 at 84.6 and Qwen3.7-Max at 89.6, with Qwen3.7-Max ahead by 5 points; SWE-bench Verified has Kimi K2.6 at 80.2 and Qwen3.7-Max at 80.4, with Qwen3.7-Max ahead by 0.2 points; SWE-bench Pro has Kimi K2.6 at 58.6 and Qwen3.7-Max at 60.6, with Qwen3.7-Max ahead by 2 points. The largest visible gap is 5 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Kimi K2.6, multimodal input: Kimi K2.6, and code execution: Qwen3.7-Max. Both models share reasoning mode, function calling, tool use, 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 K2.6 lists $0.73/1M input and $3.49/1M output tokens on the cheapest tracked provider, while Qwen3.7-Max lists $1.25/1M input and $3.75/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.6 lower by about $0.44 per million blended tokens. Availability is 9 providers versus 4, so concentration risk also matters.
Choose Kimi K2.6 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.7-Max when coding workflow support and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Kimi K2.6 or Qwen3.7-Max?
Qwen3.7-Max supports 1m tokens, while Kimi K2.6 supports 262k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, Kimi K2.6 or Qwen3.7-Max?
Kimi K2.6 is cheaper on tracked token pricing. Kimi K2.6 costs $0.73/1M input and $3.49/1M output tokens. Qwen3.7-Max costs $1.25/1M input and $3.75/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.6 or Qwen3.7-Max open source?
Kimi K2.6 is listed under MIT. Qwen3.7-Max is listed under Proprietary. 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 K2.6 or Qwen3.7-Max?
Kimi K2.6 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Kimi K2.6 or Qwen3.7-Max?
Kimi K2.6 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 K2.6 and Qwen3.7-Max?
Kimi K2.6 is available on Cloudflare Workers AI, NVIDIA NIM, Moonshot AI Kimi, Fireworks AI, and OpenRouter. Qwen3.7-Max is available on Alibaba Cloud PAI-EAS, Vercel AI Gateway, Novita AI, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.