Qwen3.5 35B A3B vs Kimi K2.7 Code
At a Glance
| Compare | Qwen3.5 35B A3BQwen | Kimi K2.7 CodeMoonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #9 of 44$0.042 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.14Deepinfra ↗ · Sep 22, 2026 | $0.68Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 22, 2026 | $3.21Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 262K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 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 | Qwen3.5-35B-A3B | Kimi K2.7 Code |
|---|---|---|
| Developer | Qwen | Moonshot AI |
| Family | Qwen3 5 35b A3b | Kimi K2 7 |
| Model | Qwen3.5-35B-A3B | Kimi K2.7 Code |
| Version | Qwen3.5-35B-A3B | Kimi K2.7 Code |
| Lifecycle | active | active |
| Released | Unknown | 2026-06-11 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 262K | 262K |
| Total parameters | 36B | 1T |
| Active parameters | 3B | 32B |
| License | apache-2.0 | modified-mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, coding, reasoning, tools, vision |
Qwen3.5 35B A3B Capabilities
Kimi K2.7 Code Capabilities
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 35B A3B vs Kimi K2.7 Code FAQs
Is Qwen3.5 35B A3B or Kimi K2.7 Code better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B and Kimi K2.7 Code, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 35B A3B or Kimi K2.7 Code?+
Qwen3.5 35B A3B is $0.14 and Kimi K2.7 Code is $0.68 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric. Qwen3.5 35B A3B is $1.00 and Kimi K2.7 Code is $3.21 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric.
Which has a larger context window, Qwen3.5 35B A3B or Kimi K2.7 Code?+
Neither model has a larger sourced context window in this comparison. Qwen3.5 35B A3B is 262K and Kimi K2.7 Code is 262K.
Which performs better in benchmarks, Qwen3.5 35B A3B or Kimi K2.7 Code?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.5 35B A3B or Kimi K2.7 Code be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 35B A3B is open weight; Kimi K2.7 Code is open weight.
Can Qwen3.5 35B A3B and Kimi K2.7 Code understand images?+
Qwen3.5 35B A3B is documented with image input; Kimi K2.7 Code is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 35B A3B or Kimi K2.7 Code?+
Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and Kimi K2.7 Code is —.
Do Qwen3.5 35B A3B and Kimi K2.7 Code support reasoning and tool use?+
Qwen3.5 35B A3B: reasoning, tool calling, and image input. Kimi K2.7 Code: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 35B A3B or Kimi K2.7 Code?+
Qwen3.5 35B A3B has 4 sourced provider routes; Kimi K2.7 Code has 4, a tie.
Which offers better value, Qwen3.5 35B A3B or Kimi K2.7 Code?+
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.