ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Gemini 3.6 Flash vs GPT-6 Luna

GPT-6 Luna comes out ahead, 78 to 71 on our weighted score, and it is the cheaper option too.

  1. Google

    Gemini 3.6 Flash

    Released Jul 21, 2026

    71/100
    • ECI154.3
    • Price$0.75 / $3.75
    • Context1.05M
    Full specs Remove Gemini 3.6 Flash
  2. Our pick

    OpenAI

    GPT-6 Luna

    Released Sep 22, 2026

    78/100
    • ECI—
    • Price$0.10 / $0.50
    • Context1.05M
    Full specs Remove GPT-6 Luna

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

GPT-6 Luna is our pick

GPT-6 Luna is the better all-round choice, scoring 78/100 against Gemini 3.6 Flash (71). It leads on price. Gemini 3.6 Flash wins on inputs & features. 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 Luna has no Capabilities Index score yet.

  • CapabilityGemini 3.6 FlashShared benchmarks: Gemini 3.6 Flash 78.4% · GPT-6 Luna 77.4%
  • Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · Gemini 3.6 Flash $1.50 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameGPT-6 Luna 1,050,000 · Gemini 3.6 Flash 1,048,576 tokens
  • Widest inputsGemini 3.6 FlashGemini 3.6 Flash: Text, Images, PDFs, Audio, Video · GPT-6 Luna: Text, Images, PDFs
  • Self-hostingNo open weightsBoth are available only through APIs
How the score is built
MeasureWeightGemini 3.6 FlashGPT-6 Luna
CapabilityShared benchmarks50%7877
Price25%4283
Inputs & features15%10080
Context window10%6161
Overall100%71/10078/100
02 — Side by side

Every spec in one table

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

Gemini 3.6 Flash vs GPT-6 Luna specifications side by side
SpecificationGemini 3.6 FlashGoogleGPT-6 LunaOpenAI
Capability
Capabilities Index (ECI)154.3—
ECI rank#34 of 148—
GPQA DiamondGraduate-level science questions94.1% (best)90.5%
FrontierMath Tiers 1–3Research-level mathematics59.0%79.0% (best)
OTIS Mock AIME 2024–2025Competition mathematics94.2%98.9% (best)
SimpleQA VerifiedShort factual questions66.2% (best)41.4%
Price per million tokens
Input$0.75$0.10 (best)
Output$3.75$0.50 (best)
Cached input$0.075$0.01 (best)
Blended (3:1)$1.50$0.20 (best)
Long-context rateSame rateOver 272K: $0.20 / $0.75
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
ReasoningYesminimal · low · medium · highYeslow · medium · high · xhigh · max
Tool callingYesYes
Structured outputYesYes
Availability
WeightsProprietaryProprietary
API model IDgemini-3.6-flashgpt-6-luna
API providers25 (best)24
ReleasedJul 21, 2026Sep 22, 2026
Knowledge cutoffMar 2026May 18, 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 3.6 Flash$15.00
  • GPT-6 Luna$2.00
04 — Questions

Which should you choose?

Which is better: Gemini 3.6 Flash or GPT-6 Luna?

GPT-6 Luna is the better all-round choice, scoring 78/100 against Gemini 3.6 Flash (71). It leads on price. Gemini 3.6 Flash wins on inputs & features. 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 Luna has no Capabilities Index score yet.

Which is cheaper, Gemini 3.6 Flash or GPT-6 Luna?

GPT-6 Luna is cheaper at $0.10 input / $0.50 output per million tokens (official OpenAI API price). Gemini 3.6 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 $0.20 per million tokens for GPT-6 Luna versus $1.50 for Gemini 3.6 Flash (7.5× 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): Gemini 3.6 Flash 78.4% and GPT-6 Luna 77.4%. On individual benchmarks: GPQA Diamond — Gemini 3.6 Flash 94.1%, GPT-6 Luna 90.5%; FrontierMath Tiers 1–3 — GPT-6 Luna 79.0%, Gemini 3.6 Flash 59.0%; OTIS Mock AIME 2024–2025 — GPT-6 Luna 98.9%, Gemini 3.6 Flash 94.2%; SimpleQA Verified — Gemini 3.6 Flash 66.2%, GPT-6 Luna 41.4%.

Which is better for coding?

There are no published SWE-bench Verified results for Gemini 3.6 Flash and GPT-6 Luna yet, so there is no like-for-like coding score. On overall capability, Gemini 3.6 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 3.6 Flash 1,048,576 and GPT-6 Luna 1,050,000 tokens. Maximum output per response: Gemini 3.6 Flash up to 65,536, GPT-6 Luna up to 128,000 tokens.

Which can read images, PDFs, audio or video?

Gemini 3.6 Flash accepts text, images, PDFs, audio and video; GPT-6 Luna accepts text, images and PDFs. Gemini 3.6 Flash handles the widest range of inputs.

Are any of these open source?

No. Gemini 3.6 Flash and GPT-6 Luna are proprietary and only available through APIs and apps.

Which is newer?

GPT-6 Luna is the newest, released Sep 22, 2026. Gemini 3.6 Flash came out Jul 21, 2026. Knowledge cutoff: Gemini 3.6 Flash Mar 2026, GPT-6 Luna May 18, 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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