Qwen3.5 397B A17B vs Llama 4 Scout 17B 16E Instruct
At a Glance
| Compare | ||
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Sep 21, 2026 | $0.10Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Sep 21, 2026 | $0.30Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 262K | 10,000K |
| 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 | Llama-4-Scout-17B-16E-Instruct |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,438.26100% of row best · rating · qwen3.5-397b-a17b; 95% CI [1434.97391785, 1441.55191176]; votes 77007; rank 71 | 1,279.2989% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.57681953, 1284.00026285]; votes 29740; rank 259 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,265.15100% of row best · rating · qwen3.5-397b-a17b; 95% CI [1259.44632362, 1270.86149272]; votes 27569; rank 42 | 1,117.8088% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114 |
| Overall ResultCounted from the protocol-matched rows above | 2 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 | Llama-4-Scout-17B-16E-Instruct |
|---|---|---|
| Developer | Qwen | Meta |
| Family | Qwen3 5 397b A17b | Llama 4 Scout 17b 16e Instruct |
| Model | Qwen3.5-397B-A17B | Llama-4-Scout-17B-16E-Instruct |
| Version | Qwen3.5-397B-A17B | Llama-4-Scout-17B-16E-Instruct |
| Lifecycle | active | active |
| Released | 2026-02-15 | 2025-04-05 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 10,000K |
| Total parameters | 403.4B | 108.6B |
| Active parameters | 17B | 17B |
| 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) | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Qwen3.5 397B A17B Capabilities
Llama 4 Scout 17B 16E Instruct Capabilities
Related Comparisons
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|---|---|---|---|
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| 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 Llama 4 Scout 17B 16E Instruct FAQs
Is Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Llama 4 Scout 17B 16E Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct?+
Qwen3.5 397B A17B is $0.45 and Llama 4 Scout 17B 16E Instruct is $0.10 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric. Qwen3.5 397B A17B is $3.00 and Llama 4 Scout 17B 16E Instruct is $0.30 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric.
Which has a larger context window, Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Qwen3.5 397B A17B supports 262K and Llama 4 Scout 17B 16E Instruct supports 10,000K.
Which performs better in benchmarks, Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 397B A17B is open weight; Llama 4 Scout 17B 16E Instruct is open weight.
Can Qwen3.5 397B A17B and Llama 4 Scout 17B 16E Instruct understand images?+
Qwen3.5 397B A17B is documented with image input; Llama 4 Scout 17B 16E Instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct?+
Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and Llama 4 Scout 17B 16E Instruct is —.
Do Qwen3.5 397B A17B and Llama 4 Scout 17B 16E Instruct support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Llama 4 Scout 17B 16E Instruct: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct?+
Qwen3.5 397B A17B has 4 sourced provider routes; Llama 4 Scout 17B 16E Instruct has 4, a tie.
Which offers better value, Qwen3.5 397B A17B or Llama 4 Scout 17B 16E Instruct?+
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.