Gemini 2.5 Pro vs GPT-5.6 Sol
At a Glance
| Compare |  Gemini 2.5 ProGoogle DeepMind |  GPT-5.6 SolOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #5 of 4685.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #24 of 44$0.117 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #3 of 3865.3 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.25Google AI ↗ · Aug 29, 2026 | $2.00Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $10.00Google AI ↗ · Aug 29, 2026 | $10.00Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,050K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 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 | GPT-5.6 Sol |
|---|---|---|
| LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · leader | 1,421.2396% of row best · rating · gemini-2.5-pro; 95% CI [1414.98072895, 1427.48825137]; votes 25110; rank 36 | 1,482.71100% of row best · rating · gpt-5.6-sol-xhigh; 95% CI [1473.91086026, 1491.51379085]; votes 4818; rank 10 |
| LMArena Search Arenasearch-2026-08-24-d25aabda0010 · arena_rating · leader | 1,141.6591% of row best · rating · gemini-2.5-pro-grounding; 95% CI [1137.05776179, 1146.24105969]; votes 83404; rank 27 | 1,257.26100% of row best · rating · gpt-5.6-sol-xhigh; 95% CI [1249.79496224, 1264.72573796]; votes 29663; rank 1 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,457.77100% of row best · rating · gemini-2.5-pro; 95% CI [1455.30542635, 1460.23799206]; votes 122554; rank 36 | 1,455.05100% of row best · rating · gpt-5.6-sol-xhigh; 95% CI [1450.16111002, 1459.93036454]; votes 27069; rank 37 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,262.1799% of row best · rating · gemini-2.5-pro; 95% CI [1257.50456844, 1266.82595195]; votes 87635; rank 45 | 1,280.13100% of row best · rating · gpt-5.6-sol-xhigh; 95% CI [1271.78485021, 1288.47570636]; votes 7729; rank 29 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 78.2789% of row best · points · Gemini 2.5 Pro · 4,521 output tokens / case | 87.97100% of row best · points · GPT-5.6 Sol (ultra) · 10,204 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 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 | GPT-5.6 Sol |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 2 5 | Gpt 5 6 |
| Model | Gemini 2.5 Pro | GPT-5.6 Sol |
| Version | Gemini 2.5 Pro | GPT-5.6 Sol |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | 2025-01-01 | 2026-02-16 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,050K |
| 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, tools |
Gemini 2.5 Pro Capabilities
GPT-5.6 Sol Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 2.5 Pro vs GPT-5.6 Sol FAQs
Is Gemini 2.5 Pro or GPT-5.6 Sol better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 Pro and GPT-5.6 Sol, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 2.5 Pro or GPT-5.6 Sol?+
Gemini 2.5 Pro is $1.25 and GPT-5.6 Sol is $2.00 per million tokens, so Gemini 2.5 Pro is cheaper on this metric. Gemini 2.5 Pro is $10.00 and GPT-5.6 Sol is $10.00 per million tokens, so they are tied on this metric.
Which has a larger context window, Gemini 2.5 Pro or GPT-5.6 Sol?+
GPT-5.6 Sol has the larger sourced context window. Gemini 2.5 Pro supports 1,049K and GPT-5.6 Sol supports 1,050K.
Which performs better in benchmarks, Gemini 2.5 Pro or GPT-5.6 Sol?+
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 GPT-5.6 Sol be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 2.5 Pro is not marked open weight; GPT-5.6 Sol is not marked open weight.
Can Gemini 2.5 Pro and GPT-5.6 Sol understand images?+
Gemini 2.5 Pro is documented with image input; GPT-5.6 Sol is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 2.5 Pro or GPT-5.6 Sol?+
GPT-5.6 Sol has the larger sourced maximum output: Gemini 2.5 Pro supports 66K and GPT-5.6 Sol supports 128K output tokens.
Do Gemini 2.5 Pro and GPT-5.6 Sol support reasoning and tool use?+
Gemini 2.5 Pro: reasoning, tool calling, and image input. GPT-5.6 Sol: 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 GPT-5.6 Sol?+
Gemini 2.5 Pro has 2 sourced provider routes; GPT-5.6 Sol has 2, a tie.
Which offers better value, Gemini 2.5 Pro or GPT-5.6 Sol?+
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.