Qwen3.5 397B A17B vs Grok 4.3
At a Glance
| Compare | Grok 4.3xAI | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #40 of 4616.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 11.1–44.4 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #7 of 44$0.028 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #33 of 3846.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 43.3–59.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Sep 21, 2026 | $1.25Xai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Sep 21, 2026 | $2.50Xai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 262K | 1,000K |
| 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
| Benchmark | Qwen3.5-397B-A17B | Grok 4.3 |
|---|---|---|
| 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 | 1,397.7397% of row best · rating · grok-4.3; 95% CI [1394.01930088, 1401.44639527]; votes 66801; rank 145 |
| 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 | 1,229.4797% of row best · rating · grok-4.3; 95% CI [1223.17337452, 1235.75913440]; votes 22688; rank 67 |
| 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 | Grok 4.3 |
|---|---|---|
| Developer | Qwen | xAI |
| Family | Qwen3 5 397b A17b | Grok 4 |
| Model | Qwen3.5-397B-A17B | Grok 4.3 |
| Version | Qwen3.5-397B-A17B | Grok 4.3 |
| Lifecycle | active | active |
| Released | 2026-02-15 | 2026-06-17 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| 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) | Xai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, structured_outputs, tools |
Qwen3.5 397B A17B Capabilities
Grok 4.3 Capabilities
Related Comparisons
All image 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 Grok 4.3 FAQs
Is Qwen3.5 397B A17B or Grok 4.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Grok 4.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Grok 4.3?+
Qwen3.5 397B A17B is $0.45 and Grok 4.3 is $1.25 per million tokens, so Qwen3.5 397B A17B is cheaper on this metric. Qwen3.5 397B A17B is $3.00 and Grok 4.3 is $2.50 per million tokens, so Grok 4.3 is cheaper on this metric.
Which has a larger context window, Qwen3.5 397B A17B or Grok 4.3?+
Grok 4.3 has the larger sourced context window. Qwen3.5 397B A17B supports 262K and Grok 4.3 supports 1,000K.
Which performs better in benchmarks, Qwen3.5 397B A17B or Grok 4.3?+
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 Grok 4.3 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; Grok 4.3 is not marked open weight.
Can Qwen3.5 397B A17B and Grok 4.3 understand images?+
Qwen3.5 397B A17B is documented with image input; Grok 4.3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Grok 4.3?+
Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and Grok 4.3 is —.
Do Qwen3.5 397B A17B and Grok 4.3 support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Grok 4.3: 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 Grok 4.3?+
Qwen3.5 397B A17B has 4 sourced provider routes; Grok 4.3 has 1, so Qwen3.5 397B A17B has broader tracked availability.
Which offers better value, Qwen3.5 397B A17B or Grok 4.3?+
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.