Gemini 2.5 Pro vs Gemini 3.1 Pro
At a Glance
| Compare |  Gemini 2.5 ProGoogle DeepMind | Gemini 3.1 ProGoogle DeepMind |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | UnrankedNot in the 57-model eligible cohort | #9 of 5782.1 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 27-model eligible cohort | #18 of 27$0.161 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | UnrankedNot in the 27-model eligible cohort | #8 of 2754.6 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.25Google AI ↗ · Aug 29, 2026 | $2.00Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $10.00Google AI ↗ · Aug 29, 2026 | $12.00Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,049K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 29, 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 2.5 Pro | Gemini 3.1 Pro |
|---|---|---|
| LMArena Document Arenadocument-2026-09-13-a64ca3b12b6e · arena_rating · leader | 1,421.2398% of row best · rating · gemini-2.5-pro; 95% CI [1414.98072895, 1427.48825137]; votes 25110; rank 36 | 1,443.92100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1438.69417039, 1449.14324048]; votes 49457; rank 31 |
| LMArena Search Arenasearch-2026-10-13-a64ca3b12b6e · arena_rating · leader | 1,142.8095% of row best · rating · gemini-2.5-pro-grounding; 95% CI [1138.12227943, 1147.47015160]; votes 86689; rank 35 | 1,208.81100% of row best · rating · gemini-3.1-pro-grounding; 95% CI [1204.12797055, 1213.48336905]; votes 151436; rank 15 |
| LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e · arena_rating · leader | 1,263.2997% of row best · rating · gemini-2.5-pro; 95% CI [1258.74589708, 1267.83852907]; votes 88805; rank 51 | 1,296.20100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1291.04958388, 1301.35691905]; votes 46320; rank 22 |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 70.96100% of row best · points · Gemini 2.5 Pro (dynamic thinking) · 4,930 output tokens / case | 70.99100% of row best · points · Gemini 3.1 Pro (high thinking) · 10,029 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 3 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 | Gemini 2.5 Pro | Gemini 3.1 Pro |
|---|---|---|
| Developer | Google DeepMind | Google DeepMind |
| Family | Gemini 2 5 | Gemini 3 |
| Model | Gemini 2.5 Pro | Gemini 3.1 Pro |
| Version | Gemini 2.5 Pro | Gemini 3.1 Pro |
| Lifecycle | active | preview |
| Released | Unknown | Unknown |
| Knowledge cutoff | 2025-01-01 | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,049K |
| 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) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Gemini 2.5 Pro Capabilities
Gemini 3.1 Pro Capabilities
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Gemini 2.5 ProGoogle DeepMind | vs | VibeVoice-ASR-Streaming-1.5BMicrosoft | cross-developer peerstext |
Gemini 2.5 ProGoogle DeepMind | vs |  GPT-5.6 LunaOpenAI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 2.5 Pro vs Gemini 3.1 Pro FAQs
Is Gemini 2.5 Pro or Gemini 3.1 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 Pro and Gemini 3.1 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 2.5 Pro or Gemini 3.1 Pro?+
Gemini 2.5 Pro is $1.25 and Gemini 3.1 Pro is $2.00 per million tokens, so Gemini 2.5 Pro is cheaper on this metric. Gemini 2.5 Pro is $10.00 and Gemini 3.1 Pro is $12.00 per million tokens, so Gemini 2.5 Pro is cheaper on this metric.
Which has a larger context window, Gemini 2.5 Pro or Gemini 3.1 Pro?+
Both models have the same sourced context window: 1,048,576 tokens each.
Which performs better in benchmarks, Gemini 2.5 Pro or Gemini 3.1 Pro?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Gemini 2.5 Pro or Gemini 3.1 Pro be self-hosted?+
Both models have the same sourced self-hosting status: unsupported. Gemini 2.5 Pro is not marked open weight; Gemini 3.1 Pro is not marked open weight.
Can Gemini 2.5 Pro and Gemini 3.1 Pro understand images?+
Gemini 2.5 Pro is documented with image input; Gemini 3.1 Pro is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 2.5 Pro or Gemini 3.1 Pro?+
Both models have the same sourced maximum output: 65,536 tokens each.
Do Gemini 2.5 Pro and Gemini 3.1 Pro support reasoning and tool use?+
Gemini 2.5 Pro: reasoning, tool calling, and image input. Gemini 3.1 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 2.5 Pro or Gemini 3.1 Pro?+
Gemini 2.5 Pro has 2 sourced provider routes; Gemini 3.1 Pro has 2, a tie.
Which offers better value, Gemini 2.5 Pro or Gemini 3.1 Pro?+
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.