Qwen3.5 397B A17B vs Qwen3.8 27B
At a Glance
| Compare |  Qwen3.8 27BQwen | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #24 of 5750.5 score · 1/3 sources · single-source provisional · missing LiveBench, ARC-AGI-2 · full-core range 16.8–83.5 | #23 of 5752.2 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 27-model eligible cohort | #12 of 27$0.063 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | UnrankedNot in the 27-model eligible cohort | #17 of 2747.3 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Oct 19, 2026 | $0.20Deepinfra ↗ · Oct 19, 2026 |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Oct 19, 2026 | $2.20Openrouter ↗ · Oct 19, 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
| Benchmark | Qwen3.5-397B-A17B | Qwen3.8-27B |
|---|---|---|
| LMArena Text Arenatext-2026-10-15-a64ca3b12b6e · arena_rating · statistical tie | 1,437.72100% of row best · rating · qwen3.5-397b-a17b; 95% CI [1434.65086689, 1440.78699055]; votes 93567; rank 81 | 1,440.55100% of row best · rating · qwen3.8-27b; 95% CI [1435.47949951, 1445.61514077]; votes 23294; rank 75 |
| LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e · arena_rating · statistical tie | 1,262.5299% of row best · rating · qwen3.5-397b-a17b; 95% CI [1257.19517602, 1267.83577721]; votes 32058; rank 54 | 1,271.39100% of row best · rating · qwen3.8-27b; 95% CI [1262.99100972, 1279.79432635]; votes 7013; rank 42 |
| Overall ResultCounted from the protocol-matched rows above · 2 ties | 0 benchmark winsNo overall winner | 0 benchmark winsNo 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 | Qwen3.8-27B |
|---|---|---|
| Developer | Qwen | Qwen |
| Family | Qwen3 5 397b A17b | Qwen3 8 27b |
| Model | Qwen3.5-397B-A17B | Qwen3.8-27B |
| Version | Qwen3.5-397B-A17B | Qwen3.8-27B |
| Lifecycle | active | active |
| Released | 2026-02-15 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 262K |
| Total parameters | 403.4B | 27.8B |
| Active parameters | 17B | Unknown |
| License | apache-2.0 | apache-2.0 |
| 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), Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Qwen3.5 397B A17B Capabilities
Qwen3.8 27B Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs | Qwen3.8-MaxQwen | family variantsimage, text | |
| vs | cross-developer peerstext | ||
| vs | family variantstext | ||
| vs |  Llama-3.1-405BMeta | cross-developer peerstext | |
| vs | cross-developer peerstext | ||
 MiniMax-M2.7MiniMax | vs | cross-developer peerstext | |
| vs |  Mistral-Medium-3.5-128BMistral AI | cross-developer peersimage, text | |
| vs |  Kimi-K2.5Moonshot AI | cross-developer peersimage, text | |
| vs | Kimi-K2.6Moonshot AI | cross-developer peersimage, text | |
Qwen3.5-35B-A3BQwen | vs | family variantsimage, text | |
| vs |  Hy3Tencent | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 397B A17B vs Qwen3.8 27B FAQs
Is Qwen3.5 397B A17B or Qwen3.8 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Qwen3.8 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Qwen3.8 27B?+
Qwen3.5 397B A17B is $0.45 and Qwen3.8 27B is $0.20 per million tokens, so Qwen3.8 27B is cheaper on this metric. Qwen3.5 397B A17B is $3.00 and Qwen3.8 27B is $2.20 per million tokens, so Qwen3.8 27B is cheaper on this metric.
Which has a larger context window, Qwen3.5 397B A17B or Qwen3.8 27B?+
Both models have the same sourced context window: 262,144 tokens each.
Which performs better in benchmarks, Qwen3.5 397B A17B or Qwen3.8 27B?+
There is no overall benchmark winner: Neither model leads outside the score tolerance on the verified common benchmarks.
Can Qwen3.5 397B A17B or Qwen3.8 27B be self-hosted?+
Both models have the same sourced self-hosting status: supported. Qwen3.5 397B A17B is open weight; Qwen3.8 27B is open weight.
Can Qwen3.5 397B A17B and Qwen3.8 27B understand images?+
Qwen3.5 397B A17B is documented with image input; Qwen3.8 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Qwen3.8 27B?+
Qwen3.8 27B has a sourced maximum output of 131,072 tokens; Qwen3.5 397B A17B's maximum output is not reported. A larger limit cannot be determined.
Do Qwen3.5 397B A17B and Qwen3.8 27B support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Qwen3.8 27B: 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 Qwen3.8 27B?+
Qwen3.5 397B A17B has 4 sourced provider routes; Qwen3.8 27B has 3, so Qwen3.5 397B A17B has broader tracked availability.
Which offers better value, Qwen3.5 397B A17B or Qwen3.8 27B?+
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.