Qwen3.5 397B A17B vs Kimi K2 Thinking
At a Glance
| Compare |  Kimi K2 ThinkingMoonshot AI | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Sep 21, 2026 | $0.60Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Sep 21, 2026 | $2.50Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 262K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Qwen3.5-397B-A17B | Kimi-K2-Thinking |
|---|---|---|
| Developer | Qwen | Moonshot AI |
| Family | Qwen3 5 397b A17b | Kimi K2 Thinking |
| Model | Qwen3.5-397B-A17B | Kimi-K2-Thinking |
| Version | Qwen3.5-397B-A17B | Kimi-K2-Thinking |
| Lifecycle | active | active |
| Released | 2026-02-15 | 2025-11-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 262K | 262K |
| Total parameters | 403.4B | 1T |
| Active parameters | 17B | 32B |
| License | apache-2.0 | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Qwen3.5 397B A17B Capabilities
Kimi K2 Thinking Capabilities
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Primary Evidence
Sources and Freshness
Questions
Qwen3.5 397B A17B vs Kimi K2 Thinking FAQs
Is Qwen3.5 397B A17B or Kimi K2 Thinking better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Kimi K2 Thinking, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Kimi K2 Thinking?+
Qwen3.5 397B A17B is $0.45 and Kimi K2 Thinking is $0.60 per million tokens, so Qwen3.5 397B A17B is cheaper on this metric. Qwen3.5 397B A17B is $3.00 and Kimi K2 Thinking is $2.50 per million tokens, so Kimi K2 Thinking is cheaper on this metric.
Which has a larger context window, Qwen3.5 397B A17B or Kimi K2 Thinking?+
Neither model has a larger sourced context window in this comparison. Qwen3.5 397B A17B is 262K and Kimi K2 Thinking is 262K.
Which performs better in benchmarks, Qwen3.5 397B A17B or Kimi K2 Thinking?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.5 397B A17B or Kimi K2 Thinking be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 397B A17B is open weight; Kimi K2 Thinking is open weight.
Can Qwen3.5 397B A17B and Kimi K2 Thinking understand images?+
Qwen3.5 397B A17B is documented with image input; Kimi K2 Thinking is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Kimi K2 Thinking?+
Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and Kimi K2 Thinking is 131K.
Do Qwen3.5 397B A17B and Kimi K2 Thinking support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Kimi K2 Thinking: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 397B A17B or Kimi K2 Thinking?+
Qwen3.5 397B A17B has 4 sourced provider routes; Kimi K2 Thinking has 2, so Qwen3.5 397B A17B has broader tracked availability.
Which offers better value, Qwen3.5 397B A17B or Kimi K2 Thinking?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.