Model comparison
Gemini 3.5 Flash vs GPT-6 Astra
GPT-6 Astra is the stronger model overall, scoring 70.3 to 54.1 on the Noometry Index. Gemini 3.5 Flash costs 5.9× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 40 shared benchmarks.
GoogleSummary
- They share 40 benchmarks with published results for both. Gemini 3.5 Flash scores higher in 3 categories and GPT-6 Astra in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Astra leads 93.4 to 60.5.
- The biggest single-benchmark swing is EBR-Bench: 4.8% for Gemini 3.5 Flash and 76.2% for GPT-6 Astra.
- Gemini 3.5 Flash is cheaper at $1.50 / $9 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.5 Flash | GPT-6 Astra | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 54.1 | 70.3 |
| Released | 2026-05-19 | 2026-09-03 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $1.50 | $10 |
| Output $ / M tokens | $9 | $50 |
| Results tracked | 54 | 51 |
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Category by category
Coding GPT-6 Astra leads
Gemini 3.5 Flash: 49.1 (#46), GPT-6 Astra: 70.5 (#3)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| DeepSWE | 37.4% | 74.1% |
| LMArena WebDev | 1499 | 1792 |
| SciCode | 53.1% | 56.5% |
| WeirdML | 62.6% | 93.6% |
| LMArena Coding | 1492 | 1488 |
| ALE-Bench | 911.02 | 2,951 |
| SWE-bench Verified | 79.3% | — |
| FrontierCode | — | 53.3% |
| FrontierSWE | — | 65.5% |
| MirrorCode | — | 46.7% |
Agentic & Tool Use GPT-6 Astra leads
Gemini 3.5 Flash: 24.8 (#107), GPT-6 Astra: 52.3 (#3)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| APEX-Agents | 27.5% | 64.7% |
| Vending-Bench 2 | 5,396 | 15,515 |
| Remote Labor Index | — | 20.8% |
| BALROG | — | 68.3% |
| GBAEval | 6.7% | — |
| GDP.pdf | 14% | — |
Reasoning GPT-6 Astra leads
Gemini 3.5 Flash: 62.7 (#16), GPT-6 Astra: 85.1 (#1)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| ARC-AGI-2 | 72.1% | 95% |
| NYT Connections (extended) | 92.6% | 98.1% |
| ARC-AGI-1 | 92.5% | 98.5% |
| CritPt | 13.1% | 31.7% |
| Chess Puzzles | 50% | 72% |
| EBR-Bench | 4.8% | 76.2% |
| LMArena Hard Prompts | 1488 | 1464 |
| Mystery Game Puzzles | 32% | 84% |
| DTBench | 94.7% | 97.3% |
| LMCA | 47.1% | 64.4% |
| Epoch Capabilities Index | 154.55 | 166.6 |
| SimpleBench | 76.7% | — |
| EnigmaEval | 25.4% | — |
| Surface Evolver Bench | 58.1% | — |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 59 | — |
Math GPT-6 Astra leads
Gemini 3.5 Flash: 60.5 (#30), GPT-6 Astra: 93.4 (#1)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| FrontierMath (Tiers 1-3) | 62.8% | 93.7% |
| FrontierMath Tier 4 | 26.8% | 97.6% |
| OTIS Mock AIME 2024-2025 | 95.6% | 100% |
| ProofBench | 31% | 99% |
| LMArena Math | 1502 | 1463 |
| MathArena Final-Answer Competitions | 76.3% | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Erdős | — | 2.9% |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge GPT-6 Astra leads
Gemini 3.5 Flash: 66.3 (#10), GPT-6 Astra: 75.3 (#1)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| GPQA Diamond | 92.8% | 95.8% |
| SimpleQA Verified | 66.2% | 75.6% |
| LMArena Expert | 1493 | 1486 |
| Humanity's Last Exam | — | 54.8% |
| Vectara Hallucination Rate | — | 8.7% |
Multimodal GPT-6 Astra leads
Gemini 3.5 Flash: 45.7 (#13), GPT-6 Astra: 55.1 (#3)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| LMArena Vision | 1311 | 1280 |
| Blueprint-Bench 2 | 33.6% | 49.7% |
| LMArena Document | 1463 | 1468 |
| Furniture Assembly | — | 80% |
Multilingual Gemini 3.5 Flash leads
Gemini 3.5 Flash: 57.1 (#12), GPT-6 Astra: 54.0 (#53)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| LMArena Non-English | 1475 | 1431 |
| LMArena Chinese | 1525 | 1495 |
| LMArena French | 1490 | 1467 |
| LMArena Russian | 1491 | 1434 |
| LMArena Spanish | 1478 | 1412 |
| LMArena German | 1500 | — |
| LMArena Japanese | 1481 | — |
| LMArena Korean | 1453 | — |
Instruction Following Too close to call
Gemini 3.5 Flash: 77.2 (#29), GPT-6 Astra: 76.6 (#37)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| LMArena Instruction Following | 1466 | 1454 |
Long Context Too close to call
Gemini 3.5 Flash: 45.3 (#39), GPT-6 Astra: 44.7 (#57)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| LMArena Longer Query | 1481 | 1460 |
Writing & Preference GPT-6 Astra leads
Gemini 3.5 Flash: 65.5 (#46), GPT-6 Astra: 75.7 (#7)
| Benchmark | Gemini 3.5 Flash | GPT-6 Astra |
|---|---|---|
| LMArena Text | 1481 | 1443 |
| LMArena Creative Writing | 1467 | 1423 |
| LMArena Multi-Turn | 1482 | 1457 |
| EQ-Bench Creative Writing | — | 2173 |
| EQ-Bench 4 | 1087 | — |
Frequently asked questions
Is Gemini 3.5 Flash better than GPT-6 Astra?
GPT-6 Astra is the stronger model overall, scoring 70.3 to 54.1 on the Noometry Index. Gemini 3.5 Flash costs 5.9× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.5 Flash or GPT-6 Astra?
Gemini 3.5 Flash is cheaper. It lists at $1.50 per million input tokens and $9 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is Gemini 3.5 Flash or GPT-6 Astra better for coding?
GPT-6 Astra scores higher on coding benchmarks: 70.5 versus 49.1 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 3.5 Flash and GPT-6 Astra share?
40 benchmarks have published results for both models. Gemini 3.5 Flash has 54 scored results on Noometry and GPT-6 Astra has 51.