Qwen3.5 35B A3B vs Claude Fable 5.1
At a Glance
| Compare |  Qwen3.5 35B A3BQwen | Claude Fable 5.1Anthropic |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #1 of 4699.0 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #43 of 44$0.824 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #22 of 3851.7 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.14Deepinfra ↗ · Sep 21, 2026 | $10.00Anthropic ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $1.00Deepinfra ↗ · Sep 21, 2026 | $50.00Anthropic ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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 | Qwen3.5-35B-A3B | Claude Fable 5.1 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,395.5093% of row best · rating · qwen3.5-35b-a3b; 95% CI [1391.20067764, 1399.79162231]; votes 29043; rank 148 | 1,507.58100% of row best · rating · claude-fable-5.1-max; 95% CI [1499.42882565, 1515.73467375]; votes 5783; rank 1 |
| 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 | Qwen3.5-35B-A3B | Claude Fable 5.1 |
|---|---|---|
| Developer | Qwen | Anthropic |
| Family | Qwen3 5 35b A3b | Claude 5 1 |
| Model | Qwen3.5-35B-A3B | Claude Fable 5.1 |
| Version | Qwen3.5-35B-A3B | Claude Fable 5.1 |
| Lifecycle | active | active |
| Released | Unknown | 2026-09-01 |
| Knowledge cutoff | Unknown | 2026-06-01 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 36B | Unknown |
| Active parameters | 3B | 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) | Anthropic (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Qwen3.5 35B A3B Capabilities
Claude Fable 5.1 Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Qwen3.5-35B-A3BQwen | vs | Claude Fable 5Anthropic | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Grok 4.6xAI | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs | Qwen3.8-MaxQwen | family variantsimage, text |
Qwen3.5-35B-A3BQwen | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Qwen3.5-35B-A3BQwen | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs |  Hy4 previewTencent | cross-developer peerstext |
Qwen3.5-35B-A3BQwen | vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text |
Qwen3.5-35B-A3BQwen | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 35B A3B vs Claude Fable 5.1 FAQs
Is Qwen3.5 35B A3B or Claude Fable 5.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B and Claude Fable 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 35B A3B or Claude Fable 5.1?+
Qwen3.5 35B A3B is $0.14 and Claude Fable 5.1 is $10.00 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric. Qwen3.5 35B A3B is $1.00 and Claude Fable 5.1 is $50.00 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric.
Which has a larger context window, Qwen3.5 35B A3B or Claude Fable 5.1?+
Claude Fable 5.1 has the larger sourced context window. Qwen3.5 35B A3B supports 262K and Claude Fable 5.1 supports 1,000K.
Which performs better in benchmarks, Qwen3.5 35B A3B or Claude Fable 5.1?+
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 Claude Fable 5.1 be self-hosted?+
Qwen3.5 35B A3B is the only model in this pair currently marked as self-hostable. Qwen3.5 35B A3B is open weight; Claude Fable 5.1 is not marked open weight.
Can Qwen3.5 35B A3B and Claude Fable 5.1 understand images?+
Qwen3.5 35B A3B is documented with image input; Claude Fable 5.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 35B A3B or Claude Fable 5.1?+
Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and Claude Fable 5.1 is 128K.
Do Qwen3.5 35B A3B and Claude Fable 5.1 support reasoning and tool use?+
Qwen3.5 35B A3B: reasoning, tool calling, and image input. Claude Fable 5.1: 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 Claude Fable 5.1?+
Qwen3.5 35B A3B has 4 sourced provider routes; Claude Fable 5.1 has 1, so Qwen3.5 35B A3B has broader tracked availability.
Which offers better value, Qwen3.5 35B A3B or Claude Fable 5.1?+
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.