Gemini 3.1 Pro vs Qwen3.7 Plus
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Gemini 3.1 Pro | Qwen3.7-Plus |
|---|---|---|
| CostLower is better · Published-token output estimate | #22 of 36$0.161 per LiveBench case | UnrankedNot in the 36-model eligible cohort |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | Gemini 3.1 Pro | Qwen3.7-Plus |
|---|---|---|
| LMArena Agent Arenaagent-2026-08-31-011508720696 · outcome_score · statistical tie | -3.13100% of row best · score · Gemini 3.1 Pro Preview; 95% CI [-4.00806078, -2.24630473]; sessions 83248; observations 2709356; rank 40 | -2.70100% of row best · score · Qwen3.7 Plus; 95% CI [-4.00378740, -1.40029439]; sessions 18748; observations 834350; rank 38 |
| LMArena Document Arenadocument-2026-07-30-011508720696 · arena_rating · statistical tie | 1,445.32100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1439.35538449, 1451.29339146]; votes 45162; rank 23 | 1,439.57100% of row best · rating · qwen3.7-plus; 95% CI [1428.95936566, 1450.17849072]; votes 3088; rank 25 |
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,480.10100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1476.89767236, 1483.30025658]; votes 102763; rank 11 | 1,453.7098% of row best · rating · qwen3.7-plus; 95% CI [1449.21174793, 1458.19774266]; votes 37854; rank 36 |
| LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · leader | 1,295.39100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1289.75236576, 1301.03738662]; votes 39412; rank 17 | 1,281.0699% of row best · rating · qwen3.7-plus; 95% CI [1273.03498970, 1289.09217677]; votes 9132; rank 26 |
| Overall ResultCounted from the protocol-matched rows above · 2 ties | 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.
Technical Differences
Side-by-Side Facts
| Field | Gemini 3.1 Pro | Qwen3.7-Plus |
|---|---|---|
| Developer | Google DeepMind | Qwen |
| Family | Gemini 3 | Qwen3 7 Plus |
| Model | Gemini 3.1 Pro | Qwen3.7-Plus |
| Version | Gemini 3.1 Pro | Qwen3.7-Plus |
| Lifecycle | preview | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 1,049K | 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 | Google AI (Standard), Google Gemini (Standard) | Alibaba Cloud Model Studio (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
13 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Gemini 3.1 Pro Capabilities
Qwen3.7 Plus Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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Gemini 3.1 ProGoogle DeepMind | vs | Kimi-K3Moonshot AI | cross-developer peerstext |
MiniMax-M3MiniMax | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Gemini 3.1 ProGoogle DeepMind | vs | GLM-5.3Z.ai | cross-developer peerstext |
Gemini 3.1 ProGoogle DeepMind | vs | Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Gemini 3.1 ProGoogle DeepMind | vs | Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Pro vs Qwen3.7 Plus FAQs
Is Gemini 3.1 Pro or Qwen3.7 Plus better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and Qwen3.7 Plus, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Pro or Qwen3.7 Plus?+
Gemini 3.1 Pro is $2.00 and Qwen3.7 Plus is $0.32 per million tokens, so Qwen3.7 Plus is cheaper on this metric. Gemini 3.1 Pro is $12.00 and Qwen3.7 Plus is $1.28 per million tokens, so Qwen3.7 Plus is cheaper on this metric.
Which has a larger context window, Gemini 3.1 Pro or Qwen3.7 Plus?+
Gemini 3.1 Pro has the larger sourced context window. Gemini 3.1 Pro supports 1,049K and Qwen3.7 Plus supports 1,000K.
Which performs better in benchmarks, Gemini 3.1 Pro or Qwen3.7 Plus?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Gemini 3.1 Pro or Qwen3.7 Plus be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.1 Pro is not marked open weight; Qwen3.7 Plus is not marked open weight.
Can Gemini 3.1 Pro and Qwen3.7 Plus understand images?+
Gemini 3.1 Pro is documented with image input; Qwen3.7 Plus is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Pro or Qwen3.7 Plus?+
Qwen3.7 Plus has the larger sourced maximum output: Gemini 3.1 Pro supports 66K and Qwen3.7 Plus supports 131K output tokens.
Do Gemini 3.1 Pro and Qwen3.7 Plus support reasoning and tool use?+
Gemini 3.1 Pro: reasoning, tool calling, and image input. Qwen3.7 Plus: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.1 Pro or Qwen3.7 Plus?+
Gemini 3.1 Pro has 2 sourced provider routes; Qwen3.7 Plus has 4, so Qwen3.7 Plus has broader tracked availability.
Which offers better value, Gemini 3.1 Pro or Qwen3.7 Plus?+
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.