Qwen3.5 397B A17B vs Gemini 3.7 Flash
At a Glance
| Compare |  Gemini 3.7 FlashGoogle DeepMind | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #4 of 4687.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #17 of 44$0.085 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #2 of 3869.9 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Sep 22, 2026 | $0.75Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Sep 22, 2026 | $3.75Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 262K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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
| Benchmark | Qwen3.5-397B-A17B | Gemini 3.7 Flash |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,438.2696% of row best · rating · qwen3.5-397b-a17b; 95% CI [1434.97391785, 1441.55191176]; votes 77007; rank 71 | 1,490.49100% of row best · rating · gemini-3.7-flash-high; 95% CI [1482.31296820, 1498.67106273]; votes 5640; rank 8 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Qwen3.5-397B-A17B | Gemini 3.7 Flash |
|---|---|---|
| Developer | Qwen | Google DeepMind |
| Family | Qwen3 5 397b A17b | Gemini 3 |
| Model | Qwen3.5-397B-A17B | Gemini 3.7 Flash |
| Version | Qwen3.5-397B-A17B | Gemini 3.7 Flash |
| Lifecycle | active | active |
| Released | 2026-02-15 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 262K | 1,049K |
| Total parameters | 403.4B | Unknown |
| Active parameters | 17B | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Qwen3.5 397B A17B Capabilities
Gemini 3.7 Flash Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text | |
| vs | Claude Fable 5.1Anthropic | cross-developer peersimage, text | |
| vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext | |
| vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text | |
| vs | Grok 4.6xAI | cross-developer peersimage, text | |
| vs |  Qwen3.8-MaxQwen | family variantsimage, text | |
| vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text | |
 MiniMax-M3MiniMax | vs | cross-developer peersimage, text | |
| vs |  GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
| vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text | |
| vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 397B A17B vs Gemini 3.7 Flash FAQs
Is Qwen3.5 397B A17B or Gemini 3.7 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Gemini 3.7 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Gemini 3.7 Flash?+
Qwen3.5 397B A17B is $0.45 and Gemini 3.7 Flash is $0.75 per million tokens, so Qwen3.5 397B A17B is cheaper on this metric. Qwen3.5 397B A17B is $3.00 and Gemini 3.7 Flash is $3.75 per million tokens, so Qwen3.5 397B A17B is cheaper on this metric.
Which has a larger context window, Qwen3.5 397B A17B or Gemini 3.7 Flash?+
Gemini 3.7 Flash has the larger sourced context window. Qwen3.5 397B A17B supports 262K and Gemini 3.7 Flash supports 1,049K.
Which performs better in benchmarks, Qwen3.5 397B A17B or Gemini 3.7 Flash?+
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 Gemini 3.7 Flash be self-hosted?+
Qwen3.5 397B A17B is the only model in this pair currently marked as self-hostable. Qwen3.5 397B A17B is open weight; Gemini 3.7 Flash is not marked open weight.
Can Qwen3.5 397B A17B and Gemini 3.7 Flash understand images?+
Qwen3.5 397B A17B is documented with image input; Gemini 3.7 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Gemini 3.7 Flash?+
Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and Gemini 3.7 Flash is 66K.
Do Qwen3.5 397B A17B and Gemini 3.7 Flash support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Gemini 3.7 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 397B A17B or Gemini 3.7 Flash?+
Qwen3.5 397B A17B has 4 sourced provider routes; Gemini 3.7 Flash has 2, so Qwen3.5 397B A17B has broader tracked availability.
Which offers better value, Qwen3.5 397B A17B or Gemini 3.7 Flash?+
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.