Gemini 3.5 Flash vs GPT-5.1
At a Glance
| Compare |  Gemini 3.5 FlashGoogle DeepMind |  GPT-5.1OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #15 of 4672.1 score · 3/3 sources · complete | #37 of 4625.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 17.1–50.5 |
| CostLower is better · Published-token output estimate | #25 of 44$0.140 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #15 of 3856.8 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.50Google AI ↗ · Aug 29, 2026 | $1.25Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $9.00Google AI ↗ · Aug 29, 2026 | $10.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,049K | 400K |
| 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 3.5 Flash | GPT-5.1 |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 92.50100% of row best · percent · Gemini 3.5 Flash (High) | 72.8379% of row best · percent · GPT-5.1 (Thinking, High) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 72.08100% of row best · percent · Gemini 3.5 Flash (High) | 17.6424% of row best · percent · GPT-5.1 (Thinking, High) |
| LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · leader | 1,461.40100% of row best · rating · gemini-3.5-flash-high; 95% CI [1452.14297727, 1470.66464631]; votes 4061; rank 22 | 1,403.1796% of row best · rating · gpt-5.1; 95% CI [1393.82886201, 1412.50542979]; votes 8244; rank 42 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,475.72100% of row best · rating · gemini-3.5-flash-medium; 95% CI [1471.24691172, 1480.19960285]; votes 36627; rank 19 | 1,422.5796% of row best · rating · gpt-5.1; 95% CI [1418.95842118, 1426.18777801]; votes 42982; rank 100 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,307.00100% of row best · rating · gemini-3.5-flash-medium; 95% CI [1299.26365925, 1314.72946839]; votes 9618; rank 11 | 1,235.0894% of row best · rating · gpt-5.1; 95% CI [1227.35514387, 1242.80648733]; votes 10205; rank 65 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 75.5899% of row best · points · Gemini 3.5 Flash (high thinking) · 16,743 output tokens / case | 75.98100% of row best · points · GPT-5.1 · 3,970 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 3.5 Flash | GPT-5.1 |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | Gpt 5 1 |
| Model | Gemini 3.5 Flash | GPT-5.1 |
| Version | Gemini 3.5 Flash | GPT-5.1 |
| Lifecycle | active | active |
| Released | Unknown | 2025-11-13 |
| Knowledge cutoff | Unknown | 2024-09-30 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 400K |
| 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 |
Gemini 3.5 Flash Capabilities
GPT-5.1 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.5 Flash vs GPT-5.1 FAQs
Is Gemini 3.5 Flash or GPT-5.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.5 Flash and GPT-5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.5 Flash or GPT-5.1?+
Gemini 3.5 Flash is $1.50 and GPT-5.1 is $1.25 per million tokens, so GPT-5.1 is cheaper on this metric. Gemini 3.5 Flash is $9.00 and GPT-5.1 is $10.00 per million tokens, so Gemini 3.5 Flash is cheaper on this metric.
Which has a larger context window, Gemini 3.5 Flash or GPT-5.1?+
Gemini 3.5 Flash has the larger sourced context window. Gemini 3.5 Flash supports 1,049K and GPT-5.1 supports 400K.
Which performs better in benchmarks, Gemini 3.5 Flash or GPT-5.1?+
Gemini 3.5 Flash leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Gemini 3.5 Flash or GPT-5.1 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.5 Flash is not marked open weight; GPT-5.1 is not marked open weight.
Can Gemini 3.5 Flash and GPT-5.1 understand images?+
Gemini 3.5 Flash is documented with image input; GPT-5.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.5 Flash or GPT-5.1?+
GPT-5.1 has the larger sourced maximum output: Gemini 3.5 Flash supports 66K and GPT-5.1 supports 128K output tokens.
Do Gemini 3.5 Flash and GPT-5.1 support reasoning and tool use?+
Gemini 3.5 Flash: reasoning, tool calling, and image input. GPT-5.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.5 Flash or GPT-5.1?+
Gemini 3.5 Flash has 2 sourced provider routes; GPT-5.1 has 2, a tie.
Which offers better value, Gemini 3.5 Flash or GPT-5.1?+
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.