Muse Spark 1.3 vs Qwen3.7 Max
At a Glance
| Compare |  Muse Spark 1.3Meta |  Qwen3.7 MaxQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #13 of 44$0.057 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.475Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.425Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,000K |
| Model facts checked | Sep 4, 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 | Muse Spark 1.3 | Qwen3.7 Max |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | 4.20100% of row best · score · Muse Spark 1.3 (Max); 95% CI [3.33950614, 5.06185896]; sessions 31052; observations 2525117; rank 15 | -3.6392% of row best · score · Qwen3.7 Max; 95% CI [-4.47002493, -2.78058186]; sessions 38392; observations 1816729; rank 34 |
| LiveBench2026-06-25 · overall · leader | 85.47100% of row best · percent · muse-spark-1.3-xhigh · 28,234 output tokens / case | 77.5091% of row best · percent · qwen3.7-max · 12,909 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 82.63100% of row best · points · Muse Spark 1.3 (thinking) · 6,301 output tokens / case | 81.2698% of row best · points · Qwen3.7 Max (default) · 8,636 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 2 benchmark winsOverall lead | 0 benchmark wins |
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 | Muse Spark 1.3 | Qwen3.7 Max |
|---|---|---|
| Developer | Meta | Qwen |
| Family | Muse Spark | Qwen3 7 |
| Model | Muse Spark 1.3 | Qwen3.7 Max |
| Version | Muse Spark 1.3 | Qwen3.7 Max |
| Lifecycle | preview | active |
| Released | 2026-09-02 | 2026-05-20 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | Unknown | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Unknown | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, computer-use, generation, reasoning, research, tools | agents, chat, generation, reasoning, structured_outputs, tools, vision |
Muse Spark 1.3 Capabilities
Qwen3.7 Max Capabilities
Related Comparisons
All image comparisons →| A | Pair | B | Context |
|---|---|---|---|
Muse Spark 1.3Meta | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Muse Spark 1.3Meta | cross-developer peersimage, text |
 Gemini 3.1 ProGoogle DeepMind | vs | Muse Spark 1.3Meta | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | Muse Spark 1.3Meta | cross-developer peersimage, text |
Muse Spark 1.3Meta | vs | Grok 4.6xAI | cross-developer peersimage, text |
Muse Spark 1.3Meta | vs | Qwen3.8-MaxQwen | cross-developer peersimage, text, video |
Muse Spark 1.3Meta | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | Muse Spark 1.3Meta | cross-developer peersimage, text |
Muse Spark 1.3Meta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Muse Spark 1.3Meta | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Muse Spark 1.3Meta | cross-developer peersimage, text, video |
Muse Spark 1.3Meta | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Muse Spark 1.3 vs Qwen3.7 Max FAQs
Is Muse Spark 1.3 or Qwen3.7 Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.3 and Qwen3.7 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Muse Spark 1.3 or Qwen3.7 Max?+
Only Qwen3.7 Max has a directly sourced input price: $1.475 per million tokens. Only Qwen3.7 Max has a directly sourced output price: $4.425 per million tokens.
Which has a larger context window, Muse Spark 1.3 or Qwen3.7 Max?+
Neither model has a larger sourced context window in this comparison. Muse Spark 1.3 is — and Qwen3.7 Max is 1,000K.
Which performs better in benchmarks, Muse Spark 1.3 or Qwen3.7 Max?+
Muse Spark 1.3 leads the current overall benchmark count. The result uses 2 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Muse Spark 1.3 or Qwen3.7 Max be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Muse Spark 1.3 is not marked open weight; Qwen3.7 Max is not marked open weight.
Can Muse Spark 1.3 and Qwen3.7 Max understand images?+
Muse Spark 1.3 is documented with image input; Qwen3.7 Max is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Muse Spark 1.3 or Qwen3.7 Max?+
Neither has a larger sourced maximum output. Muse Spark 1.3 is — and Qwen3.7 Max is 66K.
Do Muse Spark 1.3 and Qwen3.7 Max support reasoning and tool use?+
Muse Spark 1.3: reasoning, tool calling, and image input. Qwen3.7 Max: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Muse Spark 1.3 or Qwen3.7 Max?+
Muse Spark 1.3 has 0 sourced provider routes; Qwen3.7 Max has 4, so Qwen3.7 Max has broader tracked availability.
Which offers better value, Muse Spark 1.3 or Qwen3.7 Max?+
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.