Bolmo 7B vs Gemini 3.1 Pro
At a Glance
| Compare | Bolmo 7BAi2 | Gemini 3.1 ProGoogle DeepMind |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #9 of 4679.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #27 of 44$0.161 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #9 of 3859.2 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $2.00Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $12.00Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 66K | 1,049K |
| Model facts checked | Sep 3, 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
Side-by-Side Facts
| Field | Bolmo 7B | Gemini 3.1 Pro |
|---|---|---|
| Developer | Ai2 | Google DeepMind |
| Family | Bolmo | Gemini 3 |
| Model | Bolmo 7B | Gemini 3.1 Pro |
| Version | Bolmo 7B | Gemini 3.1 Pro |
| Lifecycle | active | preview |
| Released | 2025-12-13 | Unknown |
| Knowledge cutoff | 2024-12-01 | Unknown |
| Input modalities | Text | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 66K | 1,049K |
| Total parameters | 7.6B | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | generation | chat, generation, reasoning, tools |
Bolmo 7B Capabilities
Gemini 3.1 Pro Capabilities
Primary Evidence
Sources and Freshness
Questions
Bolmo 7B vs Gemini 3.1 Pro FAQs
Is Bolmo 7B or Gemini 3.1 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Bolmo 7B and Gemini 3.1 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Bolmo 7B or Gemini 3.1 Pro?+
Only Gemini 3.1 Pro has a directly sourced input price: $2.00 per million tokens. Only Gemini 3.1 Pro has a directly sourced output price: $12.00 per million tokens.
Which has a larger context window, Bolmo 7B or Gemini 3.1 Pro?+
Gemini 3.1 Pro has the larger sourced context window. Bolmo 7B supports 66K and Gemini 3.1 Pro supports 1,049K.
Which performs better in benchmarks, Bolmo 7B or Gemini 3.1 Pro?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Bolmo 7B or Gemini 3.1 Pro be self-hosted?+
Bolmo 7B is the only model in this pair currently marked as self-hostable. Bolmo 7B is open weight; Gemini 3.1 Pro is not marked open weight.
Can Bolmo 7B and Gemini 3.1 Pro understand images?+
Bolmo 7B is not 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, Bolmo 7B or Gemini 3.1 Pro?+
Neither has a larger sourced maximum output. Bolmo 7B is — and Gemini 3.1 Pro is 66K.
Do Bolmo 7B and Gemini 3.1 Pro support reasoning and tool use?+
Bolmo 7B: none of these features are definitively sourced. Gemini 3.1 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Bolmo 7B or Gemini 3.1 Pro?+
Bolmo 7B has 0 sourced provider routes; Gemini 3.1 Pro has 2, so Gemini 3.1 Pro has broader tracked availability.
Which offers better value, Bolmo 7B 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.