ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Gemini 3.8 Flash vs GPT-6.1 Sol

Gemini 3.8 Flash comes out ahead, 73 to 69 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Google

    Gemini 3.8 Flash

    Released Sep 2, 2026

    73/100
    • ECI156.9
    • Price$0.75 / $3.75
    • Context1.05M
    Full specs Remove Gemini 3.8 Flash

  2. OpenAI

    GPT-6.1 Sol

    Released Sep 29, 2026

    69/100
    • ECI—
    • Price$2.00 / $10.00
    • Context1.05M
    Full specs Remove GPT-6.1 Sol

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Gemini 3.8 Flash is our pick

Gemini 3.8 Flash is the better all-round choice, scoring 73/100 against GPT-6.1 Sol (69). It leads on price and inputs & features. GPT-6.1 Sol wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified), because GPT-6.1 Sol has no Capabilities Index score yet.

  • CapabilityGPT-6.1 SolShared benchmarks: GPT-6.1 Sol 90.7% · Gemini 3.8 Flash 83.1%
  • Lowest priceGemini 3.8 FlashGemini 3.8 Flash $1.50 · GPT-6.1 Sol $4.00 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameGPT-6.1 Sol 1,050,000 · Gemini 3.8 Flash 1,048,576 tokens
  • Widest inputsGemini 3.8 FlashGemini 3.8 Flash: Text, Images, PDFs, Audio, Video · GPT-6.1 Sol: Text, Images, PDFs
  • Self-hostingNo open weightsBoth are available only through APIs
How the score is built
MeasureWeightGemini 3.8 FlashGPT-6.1 Sol
CapabilityShared benchmarks50%8391
Price25%4221
Inputs & features15%10080
Context window10%6161
Overall100%73/10069/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Gemini 3.8 Flash vs GPT-6.1 Sol specifications side by side
SpecificationGemini 3.8 FlashGoogleGPT-6.1 SolOpenAI
Capability
Capabilities Index (ECI)156.9—
ECI rank#15 of 148—
GPQA DiamondGraduate-level science questions95.4%95.4%
FrontierMath Tiers 1–3Research-level mathematics68.4%93.7% (best)
OTIS Mock AIME 2024–2025Competition mathematics98.9%100% (best)
SimpleQA VerifiedShort factual questions69.7%73.9% (best)
Price per million tokens
Input$0.75 (best)$2.00
Output$3.75 (best)$10.00
Cached input$0.075 (best)$0.10
Blended (3:1)$1.50 (best)$4.00
Long-context rateSame rateOver 272K: $4.00 / $15.00
Price sourceOfficial Google APIOfficial OpenAI API
Limits
Context window1,048,576 tokens1,050,000 tokens (best)
Max output65,536 tokens128,000 tokens (best)
Inputs and features
TextYesYes
ImagesYesYes
PDFsYesYes
AudioYesNo
VideoYesNo
ReasoningYeslow · medium · highYeslow · medium · high · xhigh · max
Tool callingYesYes
Structured outputYesYes
Availability
WeightsProprietaryProprietary
API model IDgemini-3.8-flashgpt-6.1-sol
API providers2121
ReleasedSep 2, 2026Sep 29, 2026
Knowledge cutoff—Apr 30, 2026
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Gemini 3.8 Flash$15.00
  • GPT-6.1 Sol$40.00
04 — Questions

Which should you choose?

Which is better: Gemini 3.8 Flash or GPT-6.1 Sol?

Gemini 3.8 Flash is the better all-round choice, scoring 73/100 against GPT-6.1 Sol (69). It leads on price and inputs & features. GPT-6.1 Sol wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified), because GPT-6.1 Sol has no Capabilities Index score yet.

Which is cheaper, Gemini 3.8 Flash or GPT-6.1 Sol?

Gemini 3.8 Flash is cheaper at $0.75 input / $3.75 output per million tokens (official Google API price). GPT-6.1 Sol costs $2.00 input / $10.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $1.50 per million tokens for Gemini 3.8 Flash versus $4.00 for GPT-6.1 Sol (2.7× as much).

Which scores higher on benchmarks?

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified): GPT-6.1 Sol 90.7% and Gemini 3.8 Flash 83.1%. On individual benchmarks: GPQA Diamond — Gemini 3.8 Flash 95.4%, GPT-6.1 Sol 95.4%; FrontierMath Tiers 1–3 — GPT-6.1 Sol 93.7%, Gemini 3.8 Flash 68.4%; OTIS Mock AIME 2024–2025 — GPT-6.1 Sol 100%, Gemini 3.8 Flash 98.9%; SimpleQA Verified — GPT-6.1 Sol 73.9%, Gemini 3.8 Flash 69.7%.

Which is better for coding?

There are no published SWE-bench Verified results for Gemini 3.8 Flash and GPT-6.1 Sol yet, so there is no like-for-like coding score. On overall capability, GPT-6.1 Sol leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Which has the bigger context window?

Their context windows are effectively the same size: Gemini 3.8 Flash 1,048,576 and GPT-6.1 Sol 1,050,000 tokens. Maximum output per response: Gemini 3.8 Flash up to 65,536, GPT-6.1 Sol up to 128,000 tokens.

Which can read images, PDFs, audio or video?

Gemini 3.8 Flash accepts text, images, PDFs, audio and video; GPT-6.1 Sol accepts text, images and PDFs. Gemini 3.8 Flash handles the widest range of inputs.

Are any of these open source?

No. Gemini 3.8 Flash and GPT-6.1 Sol are proprietary and only available through APIs and apps.

Which is newer?

GPT-6.1 Sol is the newest, released Sep 29, 2026. Gemini 3.8 Flash came out Sep 2, 2026. Knowledge cutoff: GPT-6.1 Sol Apr 30, 2026.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.

05 — Keep comparing

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