Gemini 3.8 Flash vs GPT-5.6 Sol

At a Glance

Compare
Gemini 3.8 FlashGoogle DeepMind
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#8 of 4682.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 54.7–88.0#5 of 4685.3 score · 3/3 sources · complete
CostLower is better · Published-token output estimate#26 of 44$0.160 per LiveBench case#24 of 44$0.117 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#8 of 3860.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.7–63.4#3 of 3865.3 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.75Google AI · Sep 2, 2026$2.00Openrouter · Sep 22, 2026
Output priceFrom · USD / 1M tokens$3.75Google AI · Sep 2, 2026$10.00Openrouter · Sep 22, 2026
Context windowMaximum documented tokens1,049K1,050K
Model facts checkedSep 2, 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

All benchmark results →
BenchmarkGemini 3.8 FlashGPT-5.6 Sol
LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · statistical tie4.7198% of row best · score · Gemini 3.8 Flash (High); 95% CI [2.80246501, 6.61533867]; sessions 12534; observations 712246; rank 137.10100% of row best · score · GPT 5.6 Sol (xHigh); 95% CI [5.82839325, 8.38074425]; sessions 32207; observations 3553721; rank 7
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,494.67100% of row best · rating · gemini-3.8-flash-high; 95% CI [1486.13928353, 1503.20869643]; votes 5076; rank 61,455.0597% of row best · rating · gpt-5.6-sol-xhigh; 95% CI [1450.16111002, 1459.93036454]; votes 27069; rank 37
LiveBench2026-06-25 · overall · leader80.8595% of row best · percent · gemini-3.8-flash-high · 42,786 output tokens / case85.26100% of row best · percent · gpt-5.6-sol-max · 11,729 output tokens / case
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified77.8288% of row best · points · Gemini 3.8 Flash (high thinking) · 13,616 output tokens / case87.97100% of row best · points · GPT-5.6 Sol (ultra) · 10,204 output tokens / case
Overall ResultCounted from the protocol-matched rows above · 1 tie1 benchmark winNo overall winner1 benchmark winNo overall winner

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Quality Versus Estimated Output Cost

Full ranking →
Efficiency FrontierLiveBench overall · output estimate
Upper-left is better
xAIZ.aiMiniMaxDeepSeekOpenAIQwenGoogle DeepMindMoonshot AIAnthropic
LiveBench quality versus score-adjusted output costEach dot is a reviewed major-model configuration and is colored by developer. Higher means a better LiveBench overall score. Farther left means lower estimated output cost after adjusting by the score. A dotted line connects the non-dominated frontier observations. When models are selected, their sourced families remain prominent, unrelated observations retain their developer colors at lower opacity, and an orange ring identifies each selected model.$0.0050$0.010$0.050$0.100$0.500$1.006873798489Grok Build 0.1GLM 5.3 FlashDeepSeek V4.1 Flash (max)GPT-5.6 Sol (max)GPT-6 Astra (max)Claude Fable 5.1Score-adjusted output cost per LiveBench case (log) →LiveBench overall →
The dotted frontier connects measured, non-dominated major-model observations. With a selection, sourced families stay prominent, unrelated observations retain their developer colors at lower opacity, and orange rings mark the selected model or models. Family lines connect models only when their sourced family and generation match. Cost is estimated from published output tokens and the lowest current USD output rate; it excludes input, caching, batch discounts, and provider-specific benchmark execution details.

Side-by-Side Facts

FieldGemini 3.8 FlashGPT-5.6 Sol
DeveloperGoogle DeepMindOpenAI
FamilyGemini 3Gpt 5 6
ModelGemini 3.8 FlashGPT-5.6 Sol
VersionGemini 3.8 FlashGPT-5.6 Sol
Lifecycleactiveactive
Released2026-09-02Unknown
Knowledge cutoffUnknown2026-02-16
Input modalitiesText, Image, Video, Audio, DocumentText, Image
Output modalitiesTextText
Context window1,049K1,050K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, code_execution, computer_use, generation, reasoning, structured_outputs, toolschat, generation, reasoning, tools

Gemini 3.8 Flash Capabilities

chatcode executioncomputer usegenerationreasoningstructured outputstools
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.8-flash

GPT-5.6 Sol Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-5.6-sol

Primary Evidence

Sources and Freshness

Questions

Gemini 3.8 Flash vs GPT-5.6 Sol FAQs

Is Gemini 3.8 Flash or GPT-5.6 Sol better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash 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 3.8 Flash or GPT-5.6 Sol?+

Gemini 3.8 Flash is $0.75 and GPT-5.6 Sol is $2.00 per million tokens, so Gemini 3.8 Flash is cheaper on this metric. Gemini 3.8 Flash is $3.75 and GPT-5.6 Sol is $10.00 per million tokens, so Gemini 3.8 Flash is cheaper on this metric.

Which has a larger context window, Gemini 3.8 Flash or GPT-5.6 Sol?+

GPT-5.6 Sol has the larger sourced context window. Gemini 3.8 Flash supports 1,049K and GPT-5.6 Sol supports 1,050K.

Which performs better in benchmarks, Gemini 3.8 Flash or GPT-5.6 Sol?+

There is no overall benchmark winner: The verified common benchmarks do not produce a majority winner.

Can Gemini 3.8 Flash or GPT-5.6 Sol be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini 3.8 Flash is not marked open weight; GPT-5.6 Sol is not marked open weight.

Can Gemini 3.8 Flash and GPT-5.6 Sol understand images?+

Gemini 3.8 Flash 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 3.8 Flash or GPT-5.6 Sol?+

GPT-5.6 Sol has the larger sourced maximum output: Gemini 3.8 Flash supports 66K and GPT-5.6 Sol supports 128K output tokens.

Do Gemini 3.8 Flash and GPT-5.6 Sol support reasoning and tool use?+

Gemini 3.8 Flash: 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 3.8 Flash or GPT-5.6 Sol?+

Gemini 3.8 Flash has 2 sourced provider routes; GPT-5.6 Sol has 2, a tie.

Which offers better value, Gemini 3.8 Flash 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.

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