Gemini 3.1 Pro vs Inkling
At a Glance
| Compare |  Gemini 3.1 ProGoogle DeepMind | InklingThinking Machines Lab |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #9 of 4679.7 score · 3/3 sources · complete | #34 of 4645.4 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #27 of 44$0.161 per LiveBench case | #18 of 44$0.090 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #9 of 3859.2 score · 3/3 sources · complete | #28 of 3848.1 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Google AI ↗ · Aug 29, 2026 | $0.95Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $12.00Google AI ↗ · Aug 29, 2026 | $4.05Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,049K |
| Model facts checked | Aug 29, 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 | Gemini 3.1 Pro | Inkling |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 98.00100% of row best · percent · Gemini 3.1 Pro (Preview) | 79.5081% of row best · percent · Inkling |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 77.08100% of row best · percent · Gemini 3.1 Pro (Preview) | 36.5347% of row best · percent · Inkling |
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | -5.81100% of row best · score · Gemini 3.1 Pro Preview; 95% CI [-6.60162758, -5.00976712]; sessions 86017; observations 2915267; rank 38 | -10.0296% of row best · score · Inkling; 95% CI [-10.93664189, -9.10513108]; sessions 42313; observations 1624204; rank 42 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,480.08100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1476.92899018, 1483.22190450]; votes 106951; rank 16 | 1,439.6897% of row best · rating · inkling; 95% CI [1434.76600975, 1444.58550789]; votes 25922; rank 67 |
| LiveBench2026-06-25 · overall · leader | 81.66100% of row best · percent · gemini-3.1-pro-preview-high · 13,380 output tokens / case | 76.4794% of row best · percent · inkling-xhigh · 22,213 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 79.89100% of row best · points · Gemini 3.1 Pro (high thinking) · 10,009 output tokens / case | 79.4199% of row best · points · Inkling · 5,785 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 5 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 | Gemini 3.1 Pro | Inkling |
|---|---|---|
| Developer | Google DeepMind | Thinking Machines Lab |
| Family | Gemini 3 | Inkling |
| Model | Gemini 3.1 Pro | Inkling |
| Version | Gemini 3.1 Pro | Inkling |
| Lifecycle | preview | active |
| Released | Unknown | 2026-07-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Video, Audio |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,049K |
| Total parameters | Unknown | 975B |
| Active parameters | Unknown | 41B |
| License | Unknown | apache-2.0 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, coding, generation, reasoning, tools, vision |
Gemini 3.1 Pro Capabilities
Inkling Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Pro vs Inkling FAQs
Is Gemini 3.1 Pro or Inkling better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and Inkling, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Pro or Inkling?+
Gemini 3.1 Pro is $2.00 and Inkling is $0.95 per million tokens, so Inkling is cheaper on this metric. Gemini 3.1 Pro is $12.00 and Inkling is $4.05 per million tokens, so Inkling is cheaper on this metric.
Which has a larger context window, Gemini 3.1 Pro or Inkling?+
Neither model has a larger sourced context window in this comparison. Gemini 3.1 Pro is 1,049K and Inkling is 1,049K.
Which performs better in benchmarks, Gemini 3.1 Pro or Inkling?+
Gemini 3.1 Pro leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can Gemini 3.1 Pro or Inkling be self-hosted?+
Inkling is the only model in this pair currently marked as self-hostable. Gemini 3.1 Pro is not marked open weight; Inkling is open weight.
Can Gemini 3.1 Pro and Inkling understand images?+
Gemini 3.1 Pro is documented with image input; Inkling is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Pro or Inkling?+
Neither has a larger sourced maximum output. Gemini 3.1 Pro is 66K and Inkling is —.
Do Gemini 3.1 Pro and Inkling support reasoning and tool use?+
Gemini 3.1 Pro: reasoning, tool calling, and image input. Inkling: 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 Inkling?+
Gemini 3.1 Pro has 2 sourced provider routes; Inkling has 4, so Inkling has broader tracked availability.
Which offers better value, Gemini 3.1 Pro or Inkling?+
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.