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Comparison · 2 models · Updated Oct 4, 2026

Gemini 3.8 Flash vs GPT-5.6 Luna

Too close to call on our weighted score (GPT-5.6 Luna 78, Gemini 3.8 Flash 75). The right pick depends on what you value most.

  1. Google

    Gemini 3.8 Flash

    Released Sep 2, 2026

    75/100
    • ECI156.9
    • Price$0.75 / $3.75
    • Context1.05M
    Full specs Remove Gemini 3.8 Flash

  2. OpenAI

    GPT-5.6 Luna

    Released Jul 9, 2026

    78/100
    • ECI156.5
    • Price$0.20 / $1.20
    • Context1.05M
    Full specs Remove GPT-5.6 Luna

  3. Add a model

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01 — Verdict

Too close to call

It is close. Our weighted score puts them within 3 points (GPT-5.6 Luna 78/100, Gemini 3.8 Flash 75/100), so choose by what matters most for your work: Gemini 3.8 Flash for raw capability and GPT-5.6 Luna on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGemini 3.8 FlashCapabilities Index (ECI): Gemini 3.8 Flash 156.9 · GPT-5.6 Luna 156.5
  • Lowest priceGPT-5.6 LunaGPT-5.6 Luna $0.45 · Gemini 3.8 Flash $1.50 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameGPT-5.6 Luna 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-5.6 Luna: Text, Images, PDFs
  • Self-hostingNo open weightsBoth are available only through APIs
How the score is built
MeasureWeightGemini 3.8 FlashGPT-5.6 Luna
CapabilityCapabilities Index (ECI)50%8786
Price25%4266
Inputs & features15%10080
Context window10%6161
Overall100%75/10078/100
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-5.6 Luna specifications side by side
SpecificationGemini 3.8 FlashGoogleGPT-5.6 LunaOpenAI
Capability
Capabilities Index (ECI)156.9 (best)156.5
ECI rank#15 of 148 (best)#21 of 148
GPQA DiamondGraduate-level science questions95.4% (best)91.6%
FrontierMath Tiers 1–3Research-level mathematics68.4%82.1% (best)
OTIS Mock AIME 2024–2025Competition mathematics98.9% (best)98.3%
SimpleQA VerifiedShort factual questions69.7% (best)41.0%
Price per million tokens
Input$0.75$0.20 (best)
Output$3.75$1.20 (best)
Cached input$0.075$0.02 (best)
Blended (3:1)$1.50$0.45 (best)
Long-context rateSame rateOver 272K: $0.40 / $1.80
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-5.6-luna
API providers2138 (best)
ReleasedSep 2, 2026Jul 9, 2026
Knowledge cutoff—Feb 16, 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-5.6 Luna$4.40
04 — Questions

Which should you choose?

Which is better: Gemini 3.8 Flash or GPT-5.6 Luna?

It is close. Our weighted score puts them within 3 points (GPT-5.6 Luna 78/100, Gemini 3.8 Flash 75/100), so choose by what matters most for your work: Gemini 3.8 Flash for raw capability and GPT-5.6 Luna on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Gemini 3.8 Flash or GPT-5.6 Luna?

GPT-5.6 Luna is cheaper at $0.20 input / $1.20 output per million tokens (official OpenAI API price). Gemini 3.8 Flash costs $0.75 input / $3.75 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for GPT-5.6 Luna versus $1.50 for Gemini 3.8 Flash (3.3× as much).

Which scores higher on benchmarks?

Gemini 3.8 Flash scores higher on the Capabilities Index (ECI): Gemini 3.8 Flash 156.9 (#15 of 148) and GPT-5.6 Luna 156.5 (#21 of 148). The confidence ranges of the top two overlap (154.6–160.4 vs 154.1–158.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemini 3.8 Flash 95.4%, GPT-5.6 Luna 91.6%; FrontierMath Tiers 1–3 — GPT-5.6 Luna 82.1%, Gemini 3.8 Flash 68.4%; OTIS Mock AIME 2024–2025 — Gemini 3.8 Flash 98.9%, GPT-5.6 Luna 98.3%; SimpleQA Verified — Gemini 3.8 Flash 69.7%, GPT-5.6 Luna 41.0%.

Which is better for coding?

There are no published SWE-bench Verified results for Gemini 3.8 Flash and GPT-5.6 Luna yet, so there is no like-for-like coding score. On overall capability, Gemini 3.8 Flash 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-5.6 Luna 1,050,000 tokens. Maximum output per response: Gemini 3.8 Flash up to 65,536, GPT-5.6 Luna 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-5.6 Luna 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-5.6 Luna are proprietary and only available through APIs and apps.

Which is newer?

Gemini 3.8 Flash is the newest, released Sep 2, 2026. GPT-5.6 Luna came out Jul 9, 2026. Knowledge cutoff: GPT-5.6 Luna Feb 16, 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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