Model comparison
Gemini 1.5 Flash (May 2024) vs GPT-5.3 Codex
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 32.9 on the Noometry Index.
Last verified . 2 shared benchmarks.
GoogleSummary
- They share 2 benchmarks with published results for both. Gemini 1.5 Flash (May 2024) scores higher in 0 categories and GPT-5.3 Codex in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 47.8 to 26.3.
- The biggest single-benchmark swing is WeirdML: 24.9% for Gemini 1.5 Flash (May 2024) and 79.3% for GPT-5.3 Codex.
Side by side
| Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 32.9 | 45.5 |
| Released | 2024-05-14 | 2026-02-05 |
| Weights | Proprietary | Proprietary |
| Context window | — | 400K |
| Max output | — | 128K |
| Input $ / M tokens | — | $1.75 |
| Output $ / M tokens | — | $14 |
| Results tracked | 42 | 8 |
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Category by category
Coding GPT-5.3 Codex leads
Gemini 1.5 Flash (May 2024): 33.9 (#233), GPT-5.3 Codex: 48.5 (#51)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| WeirdML | 24.9% | 79.3% |
| SWE-bench Verified | — | 74.8% |
| LMArena WebDev | — | 1408 |
| BigCodeBench Instruct | 43.5% | — |
| LMArena Coding | 1262 | — |
| BigCodeBench Complete | 55.1% | — |
| ALE-Bench | — | 1,655 |
| HumanEval+ | 75.6% | — |
| MBPP+ | 67.5% | — |
Agentic & Tool Use GPT-5.3 Codex leads
Gemini 1.5 Flash (May 2024): 26.3 (#97), GPT-5.3 Codex: 47.8 (#9)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | — | 78.4% |
| BALROG | 14.6% | — |
| METR Time Horizons | — | 74.5% |
| Vending-Bench 2 | — | 5,940 |
Reasoning Not comparable
Gemini 1.5 Flash (May 2024): 20.8 (#212), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 129.37 | 156.84 |
| LMArena Hard Prompts | 1257 | — |
| DTBench | 53.8% | — |
| ForecastBench | 53.9 | — |
| PIQA | 87.5% | — |
Math Not comparable
Gemini 1.5 Flash (May 2024): 20.8 (#274), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.3% | — |
| Omni-MATH | 30.4% | — |
| LMArena Math | 1269 | — |
| MATH Level 5 | 61.9% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
| GSM8K | 82.4% | — |
Knowledge Not comparable
Gemini 1.5 Flash (May 2024): 26.2 (#253), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 47.3% | — |
| MMLU-Pro | 67.8% | — |
| GPQA (HELM) | 43.7% | — |
| LMArena Expert | 1233 | — |
| BoolQ | 85.8% | — |
| MMLU | 77.9% | — |
Multimodal Not comparable
Gemini 1.5 Flash (May 2024): 35.3 (#76), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| LMArena Vision | 1141 | — |
| Video-MME | 70.3% | — |
| GeoBench | 76% | — |
Multilingual Not comparable
Gemini 1.5 Flash (May 2024): 43.1 (#184), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1278 | — |
| LMArena Chinese | 1295 | — |
| LMArena French | 1257 | — |
| LMArena German | 1262 | — |
| LMArena Japanese | 1252 | — |
| LMArena Korean | 1220 | — |
| LMArena Russian | 1288 | — |
| LMArena Spanish | 1242 | — |
Instruction Following Not comparable
Gemini 1.5 Flash (May 2024): 67.0 (#199), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| IFEval | 83.1% | — |
| LMArena Instruction Following | 1258 | — |
Long Context Not comparable
Gemini 1.5 Flash (May 2024): 39.0 (#182), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1284 | — |
Writing & Preference Not comparable
Gemini 1.5 Flash (May 2024): 48.9 (#190), GPT-5.3 Codex: —
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1287 | — |
| LMArena Creative Writing | 1285 | — |
| WildBench | 79.2% | — |
| LMArena Multi-Turn | 1253 | — |
Frequently asked questions
Is Gemini 1.5 Flash (May 2024) better than GPT-5.3 Codex?
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 32.9 on the Noometry Index.
Is Gemini 1.5 Flash (May 2024) or GPT-5.3 Codex better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 33.9 in the Noometry coding category.
How many benchmarks do Gemini 1.5 Flash (May 2024) and GPT-5.3 Codex share?
2 benchmarks have published results for both models. Gemini 1.5 Flash (May 2024) has 42 scored results on Noometry and GPT-5.3 Codex has 8.