Model comparison
Gemini 3.5 Flash vs GPT-5-Codex
Gemini 3.5 Flash is the stronger model overall, scoring 54.2 to 37.9 on the Noometry Index.
Last verified . 1 shared benchmarks.
GoogleSummary
- They share 1 benchmark with published results for both. Gemini 3.5 Flash scores higher in 2 categories and GPT-5-Codex in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.5 Flash leads 62.8 to 30.9.
- The biggest single-benchmark swing is WeirdML: 62.6% for Gemini 3.5 Flash and 54.5% for GPT-5-Codex.
- Both cost about the same: $1.50 input and $9 output per million tokens.
- Gemini 3.5 Flash accepts more context: 1.05M tokens versus 400K.
Side by side
| Gemini 3.5 Flash | GPT-5-Codex | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 54.2 | 37.9 |
| Released | 2026-05-19 | 2025-09-15 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 400K |
| Max output | 66K | 128K |
| Input $ / M tokens | $1.50 | $1.25 |
| Output $ / M tokens | $9 | $10 |
| Results tracked | 54 | 3 |
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Category by category
Coding Gemini 3.5 Flash leads
Gemini 3.5 Flash: 49.4 (#49), GPT-5-Codex: 42.4 (#103)
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| WeirdML | 62.6% | 54.5% |
| SWE-bench Verified | 79.3% | — |
| DeepSWE | 37.4% | — |
| LMArena WebDev | 1499 | — |
| SciCode | 53.1% | — |
| LMArena Coding | 1492 | — |
| ALE-Bench | 911.02 | — |
Agentic & Tool Use GPT-5-Codex leads
Gemini 3.5 Flash: 24.7 (#114), GPT-5-Codex: 31.0 (#72)
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| Terminal-Bench | — | 44.3% |
| APEX-Agents | 27.5% | — |
| GBAEval | 6.7% | — |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | 5,396 | — |
Reasoning Gemini 3.5 Flash leads
Gemini 3.5 Flash: 62.8 (#18), GPT-5-Codex: 30.9 (#83)
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 76.7% | — |
| Kagi LLM Benchmark | — | 70.3% |
| NYT Connections (extended) | 92.6% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 13.1% | — |
| Chess Puzzles | 50% | — |
| EnigmaEval | 25.4% | — |
| EBR-Bench | 4.8% | — |
| LMArena Hard Prompts | 1488 | — |
| Mystery Game Puzzles | 32% | — |
| DTBench | 94.7% | — |
| LMCA | 47.1% | — |
| Surface Evolver Bench | 58.1% | — |
| Epoch Capabilities Index | 154.46 | — |
| ForecastBench | 59 | — |
Math Not comparable
Gemini 3.5 Flash: 60.7 (#36), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 62.8% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.3% | — |
| OTIS Mock AIME 2024-2025 | 95.6% | — |
| ProofBench | 31% | — |
| LMArena Math | 1504 | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Not comparable
Gemini 3.5 Flash: 66.3 (#11), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| GPQA Diamond | 92.8% | — |
| SimpleQA Verified | 66.2% | — |
| LMArena Expert | 1495 | — |
Multimodal Not comparable
Gemini 3.5 Flash: 45.7 (#15), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| LMArena Vision | 1310 | — |
| Blueprint-Bench 2 | 33.6% | — |
| LMArena Document | 1463 | — |
Multilingual Not comparable
Gemini 3.5 Flash: 57.0 (#13), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| LMArena Non-English | 1476 | — |
| LMArena Chinese | 1526 | — |
| LMArena French | 1490 | — |
| LMArena German | 1492 | — |
| LMArena Japanese | 1486 | — |
| LMArena Korean | 1451 | — |
| LMArena Russian | 1493 | — |
| LMArena Spanish | 1480 | — |
Instruction Following Not comparable
Gemini 3.5 Flash: 77.0 (#30), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| LMArena Instruction Following | 1467 | — |
Long Context Not comparable
Gemini 3.5 Flash: 45.4 (#38), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference Not comparable
Gemini 3.5 Flash: 65.5 (#47), GPT-5-Codex: —
| Benchmark | Gemini 3.5 Flash | GPT-5-Codex |
|---|---|---|
| LMArena Text | 1482 | — |
| LMArena Creative Writing | 1470 | — |
| EQ-Bench 4 | 1087 | — |
| LMArena Multi-Turn | 1481 | — |
Frequently asked questions
Is Gemini 3.5 Flash better than GPT-5-Codex?
Gemini 3.5 Flash is the stronger model overall, scoring 54.2 to 37.9 on the Noometry Index.
Which is cheaper, Gemini 3.5 Flash or GPT-5-Codex?
Gemini 3.5 Flash is cheaper. It lists at $1.50 per million input tokens and $9 per million output tokens; GPT-5-Codex lists at $1.25 and $10.
Is Gemini 3.5 Flash or GPT-5-Codex better for coding?
Gemini 3.5 Flash scores higher on coding benchmarks: 49.4 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.5 Flash does, with 1.05M tokens against 400K.
How many benchmarks do Gemini 3.5 Flash and GPT-5-Codex share?
1 benchmark has published results for both models. Gemini 3.5 Flash has 54 scored results on Noometry and GPT-5-Codex has 3.