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Model comparison

Gemini 3.1 Pro vs GPT-5.6 Sol

Google against OpenAI, compared on context, price, and attributed benchmark results.

Recorded profile verification October 1, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

Google

Gemini 3.1 Pro

Frontier

vs

OpenAI

GPT-5.6 Sol

Frontier

AI model capability comparison
SpecificationGemini 3.1 ProGPT-5.6 Sol
ProviderGoogleOpenAI
TierFrontierFrontier
Context window1.05MWinner: 1.05M
Max output66KWinner: 128K
Input / 1M tokensWinner: $2$4
Output / 1M tokensWinner: $12$20
WeightsClosedClosed
ParametersNot verifiedUnverifiedNot verifiedUnverified
Reasoning levelslow, medium, highnone, low, medium, high, xhigh, max
Modalitiestext, image, video, audio, pdftext, image
API model idgemini-3.1-pro-previewgpt-5.6-sol
ReleasedFebruary 19, 2026July 9, 2026
SWE-bench Verified · source date Gemini 3.1 Pro: 2026-0280.6Not verifiedUnverified
GPQA Diamond · source date Gemini 3.1 Pro: 2026-02, GPT-5.6 Sol: 2026-07-09 (comparability not verified)94.394.6
Humanity's Last Exam · source date Gemini 3.1 Pro: 2026-0244.4Not verifiedUnverified
Terminal-Bench 2.1 · source date Gemini 3.1 Pro: 2026-07, GPT-5.6 Sol: 2026-07-09 (comparability not verified)73.888.8

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • Source date2026-07-09
    OpenAI — GPT-5.6 evals table (vendor)

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-07-09
    OpenAI — GPT-5.6 evals table (vendor; Ultra multi-agent config: 91.9)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-07
    Google DeepMind model evaluation report (vendor, Terminus-2; own Feb report separately reported TB 2.0 = 68.5 — versions preserved)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-02
    Google DeepMind model evaluation report (vendor, no tools; 51.4 with search+code)

    Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.

    Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

  • Source date2026-02
    Google DeepMind model evaluation report (vendor, no tools, pass@1)

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-02
    Google DeepMind model evaluation report (vendor, Thinking High, avg of 10 runs, +0.6 disclosed adjustment)

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

Pricing tiers

Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable.

GPT-5.6 Sol: Launch $5/$30, reduced to promotional $4/$20 on August 21; available at least through November 21, 2026. Above 272K input the full request bills $8/$30. Cached input $0.40/$0.80 short/long and cache writes $5/$10. Batch is $2/$10 at short context. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Feb 16 2026.

FrontierRecord checked October 1, 2026

Gemini 3.1 Pro

Gemini 3.1 Pro Preview is Google's multimodal Pro endpoint with a 1,048,576-token input limit and 65,536-token output limit.

Best for

  • Very long documents
  • Multimodal reasoning
  • Frontier knowledge work

Watch out

This API endpoint remains Preview. A separate customtools endpoint is optimized for bash/custom-tool workflows and can behave differently on other tasks. Keep endpoint, thinking level and benchmark version matched; restricted Gemini 4 Argon access is a separate lifecycle.

FrontierRecord checked October 1, 2026

GPT-5.6 Sol

GPT-5.6 Sol was OpenAI's GPT-5.6 flagship for complex reasoning and coding with a 1.05M-token context — superseded on 2026-09-22 by GPT-6 Sol at half the price.

Best for

  • Complex reasoning
  • Agentic coding
  • Hard knowledge work

Watch out

GPT-6.1 Sol is the newer Sol option at $2/$10 Standard. GPT-5.6 Sol remains listed; its $4/$20 price is promotional. Max effort can increase token use, and Ultra is a multi-agent configuration rather than a separate reasoning.effort value.

When the cheaper one wins

Gemini 3.1 Pro is cheaper on output at $12 per million tokens against $20 for GPT-5.6 Sol — about 1.7×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, Gemini 3.1 Pro is 11.7% Pass@1 at $2.14/task versus GPT-5.6 Sol at 72.7% / $8.39/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

Run the model picker

Evidence confidence: Medium

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? Gemini 3.1 Pro · GPT-5.6 Sol

Common questions

Gemini 3.1 Pro vs GPT-5.6 Sol

Answered from the verified figures on this page rather than general guidance.

Is Gemini 3.1 Pro or GPT-5.6 Sol cheaper for input?
Gemini 3.1 Pro is cheaper at $2 per million input tokens, against $4 for GPT-5.6 Sol — roughly 2.0× the price. Estimate both input and output token volume for your workload. These are base rates; Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable. GPT-5.6 Sol: Launch $5/$30, reduced to promotional $4/$20 on August 21; available at least through November 21, 2026. Above 272K input the full request bills $8/$30. Cached input $0.40/$0.80 short/long and cache writes $5/$10. Batch is $2/$10 at short context. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Feb 16 2026.
Is Gemini 3.1 Pro or GPT-5.6 Sol cheaper for output?
Gemini 3.1 Pro is cheaper at $12 per million output tokens, against $20 for GPT-5.6 Sol — roughly 1.7× the price. Estimate both input and output token volume for your workload. These are base rates; Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable. GPT-5.6 Sol: Launch $5/$30, reduced to promotional $4/$20 on August 21; available at least through November 21, 2026. Above 272K input the full request bills $8/$30. Cached input $0.40/$0.80 short/long and cache writes $5/$10. Batch is $2/$10 at short context. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Feb 16 2026.
Which has the larger context window, Gemini 3.1 Pro or GPT-5.6 Sol?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Do Gemini 3.1 Pro and GPT-5.6 Sol support the same reasoning levels?
Gemini 3.1 Pro exposes low, medium, high, while GPT-5.6 Sol exposes none, low, medium, high, xhigh, max.
Should I use Gemini 3.1 Pro or GPT-5.6 Sol?
Both are classified in our frontier tier; this editorial grouping does not imply equal capability. Gemini 3.1 Pro is a candidate for very long documents; GPT-5.6 Sol is a candidate for complex reasoning. Compare accuracy, tool reliability, latency and cost on representative tasks.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: Gemini 3.1 Pro costs $2 per 1M tokens versus $4 for GPT-5.6 Sol — a 2x difference at the headline tier.
  • Context: GPT-5.6 Sol takes 1.05M against 1.05M for Gemini 3.1 Pro — only decisive if your prompts approach the smaller window.
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