Gemini 3.1 Pro vs o3
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Gemini 3.1 Pro | o3 |
|---|---|---|
| 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
Technical Differences
Side-by-Side Facts
| Field | Gemini 3.1 Pro | o3 |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | O3 |
| Model | Gemini 3.1 Pro | o3 |
| Version | Gemini 3.1 Pro | o3 |
| Lifecycle | preview | active |
| Released | Unknown | 2025-04-16 |
| Knowledge cutoff | Unknown | 2024-06-01 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 200K |
| 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) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, structured_outputs, tools |
13 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Gemini 3.1 Pro Capabilities
o3 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
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Gemini 3.1 ProGoogle DeepMind | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
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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 o3 FAQs
Is Gemini 3.1 Pro or o3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Pro and o3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Pro or o3?+
Gemini 3.1 Pro is $2.00 and o3 is $1.00 per million tokens, so o3 is cheaper on this metric. Gemini 3.1 Pro is $12.00 and o3 is $4.00 per million tokens, so o3 is cheaper on this metric.
Which has a larger context window, Gemini 3.1 Pro or o3?+
Gemini 3.1 Pro has the larger sourced context window. Gemini 3.1 Pro supports 1,049K and o3 supports 200K.
Which performs better in benchmarks, Gemini 3.1 Pro or o3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.1 Pro or o3 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.1 Pro is not marked open weight; o3 is not marked open weight.
Can Gemini 3.1 Pro and o3 understand images?+
Gemini 3.1 Pro is documented with image input; o3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.1 Pro or o3?+
o3 has the larger sourced maximum output: Gemini 3.1 Pro supports 66K and o3 supports 100K output tokens.
Do Gemini 3.1 Pro and o3 support reasoning and tool use?+
Gemini 3.1 Pro: reasoning, tool calling, and image input. o3: 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 o3?+
Gemini 3.1 Pro has 2 sourced provider routes; o3 has 2, a tie.
Which offers better value, Gemini 3.1 Pro or o3?+
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.