Gemini 3.8 Flash vs GPT-6 Luna
GPT-6 Luna comes out ahead, 78 to 73 on our weighted score, and it is the cheaper option too.
GoogleGemini 3.8 Flash
73/100- ECI156.9
- Price$0.75 / $3.75
- Context1.05M
- Our pick
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
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Make it a three-way comparison.
GPT-6 Luna is our pick
GPT-6 Luna is the better all-round choice, scoring 78/100 against Gemini 3.8 Flash (73). It leads on price. Gemini 3.8 Flash wins on capability and 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 3.8 FlashShared benchmarks: Gemini 3.8 Flash 83.1% · GPT-6 Luna 77.4%
- Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · Gemini 3.8 Flash $1.50 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGPT-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-6 Luna: Text, Images, PDFs
- Self-hostingNo open weightsBoth are available only through APIs
| Measure | Weight | Gemini 3.8 Flash | GPT-6 Luna |
|---|---|---|---|
| CapabilityShared benchmarks | 50% | 83 | 77 |
| Price | 25% | 42 | 83 |
| Inputs & features | 15% | 100 | 80 |
| Context window | 10% | 61 | 61 |
| Overall | 100% | 73/100 | 78/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 3.8 FlashGoogle | GPT-6 LunaOpenAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 156.9 | — |
| ECI rank | #15 of 148 | — |
| GPQA DiamondGraduate-level science questions | 95.4% (best) | 90.5% |
| FrontierMath Tiers 1–3Research-level mathematics | 68.4% | 79.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 98.9% | 98.9% |
| SimpleQA VerifiedShort factual questions | 69.7% (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 rate | Same rate | Over 272K: $0.20 / $0.75 |
| Price source | Official Google API | Official OpenAI API |
| Limits | ||
| Context window | 1,048,576 tokens | 1,050,000 tokens (best) |
| Max output | 65,536 tokens | 128,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | Yes | Yes |
| Audio | Yes | No |
| Video | Yes | No |
| Reasoning | Yeslow · medium · high | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | Proprietary |
| API model ID | gemini-3.8-flash | gpt-6-luna |
| API providers | 21 | 24 (best) |
| Released | Sep 2, 2026 | Sep 22, 2026 |
| Knowledge cutoff | — | May 18, 2026 |
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 Luna$2.00
Which should you choose?
Which is better: Gemini 3.8 Flash or GPT-6 Luna?
GPT-6 Luna is the better all-round choice, scoring 78/100 against Gemini 3.8 Flash (73). It leads on price. Gemini 3.8 Flash wins on capability and 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 3.8 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 3.8 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 $0.20 per million tokens for GPT-6 Luna versus $1.50 for Gemini 3.8 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 3.8 Flash 83.1% and GPT-6 Luna 77.4%. On individual benchmarks: GPQA Diamond — Gemini 3.8 Flash 95.4%, GPT-6 Luna 90.5%; FrontierMath Tiers 1–3 — GPT-6 Luna 79.0%, Gemini 3.8 Flash 68.4%; OTIS Mock AIME 2024–2025 — Gemini 3.8 Flash 98.9%, GPT-6 Luna 98.9%; SimpleQA Verified — Gemini 3.8 Flash 69.7%, GPT-6 Luna 41.4%.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 3.8 Flash and GPT-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-6 Luna 1,050,000 tokens. Maximum output per response: Gemini 3.8 Flash up to 65,536, GPT-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-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-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 3.8 Flash came out Sep 2, 2026. Knowledge cutoff: 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.
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