Model comparison
Gemini 1.5 Flash (May 2024) vs GPT-5.5
GPT-5.5 is the stronger model overall, scoring 63.4 to 33.2 on the Noometry Index.
Last verified . 25 shared benchmarks.
GoogleSummary
- They share 25 benchmarks with published results for both. Gemini 1.5 Flash (May 2024) scores higher in 0 categories and GPT-5.5 in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.5 leads 81.7 to 22.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 16.3% for Gemini 1.5 Flash (May 2024) and 100% for GPT-5.5.
Side by side
| Gemini 1.5 Flash (May 2024) | GPT-5.5 | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 33.2 | 63.4 |
| Released | 2024-05-14 | 2026-04-23 |
| Weights | Proprietary | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $5 |
| Output $ / M tokens | — | $30 |
| Results tracked | 42 | 71 |
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Category by category
Coding GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 34.4 (#236), GPT-5.5: 58.2 (#17)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| WeirdML | 24.9% | 84.9% |
| LMArena Coding | 1261 | 1494 |
| SWE-bench Verified | — | 80.6% |
| DeepSWE | — | 67% |
| FrontierCode | — | 43% |
| LMArena WebDev | — | 1513 |
| SciCode | — | 56.1% |
| GSO | — | 40.2% |
| BigCodeBench Instruct | 43.5% | — |
| MirrorCode | — | 10% |
| BigCodeBench Complete | 55.1% | — |
| ALE-Bench | — | 1,943 |
| HumanEval+ | 75.6% | — |
| MBPP+ | 67.5% | — |
Agentic & Tool Use GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 26.6 (#102), GPT-5.5: 50.7 (#6)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| Terminal-Bench | — | 84.7% |
| APEX-Agents | — | 55.1% |
| OSWorld 2.0 | — | 13% |
| Remote Labor Index | — | 6.3% |
| τ²-bench Banking | — | 44.6% |
| DeepResearch Bench | — | 54% |
| PostTrainBench | — | 27.2% |
| BALROG | 14.6% | — |
| ExploitBench | — | 47.4% |
| GBAEval | — | 53.2% |
| GDP.pdf | — | 26% |
| LMArena Search | — | 1242 |
| Vending-Bench 2 | — | 7,524 |
Reasoning GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 21.7 (#215), GPT-5.5: 72.8 (#11)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1489 |
| DTBench | 53.8% | 96% |
| Epoch Capabilities Index | 129.36 | 159.1 |
| ForecastBench | 53.9 | 60.6 |
| ARC-AGI-2 | — | 85% |
| SimpleBench | — | 69% |
| Kagi LLM Benchmark | — | 88.8% |
| NYT Connections (extended) | — | 96.2% |
| ARC-AGI-1 | — | 95% |
| CritPt | — | 27.1% |
| Chess Puzzles | — | 54% |
| EBR-Bench | — | 34.3% |
| Mystery Game Puzzles | — | 56% |
| LMCA | — | 54.3% |
| Surface Evolver Bench | — | 88.1% |
| Bench to the Future 3 | — | 0.14 |
| PIQA | 87.5% | — |
Math GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 22.1 (#281), GPT-5.5: 81.7 (#11)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.3% | 100% |
| LMArena Math | 1269 | 1486 |
| FrontierMath (Feb 2025 set) | 0% | 51.7% |
| FrontierMath (Tiers 1-3) | — | 85.3% |
| FrontierMath Tier 4 | — | 72.5% |
| MathArena Final-Answer Competitions | — | 94.3% |
| ProofBench | — | 50% |
| Omni-MATH | 30.4% | — |
| MATH Level 5 | 61.9% | — |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | — | 35.4% |
| GSM8K | 82.4% | — |
Knowledge GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 26.2 (#260), GPT-5.5: 64.4 (#17)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| GPQA Diamond | 47.3% | 94% |
| LMArena Expert | 1233 | 1508 |
| SimpleQA Verified | — | 63% |
| MMLU-Pro | 67.8% | — |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | 43.7% | — |
| BoolQ | 85.8% | — |
| MMLU | 77.9% | — |
Multimodal GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 36.0 (#81), GPT-5.5: 46.9 (#12)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| LMArena Vision | 1141 | 1297 |
| Video-MME | 70.3% | — |
| GeoBench | 76% | — |
| Blueprint-Bench 2 | — | 36.2% |
| Furniture Assembly | — | 44.2% |
| LMArena Document | — | 1486 |
Multilingual GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 42.9 (#189), GPT-5.5: 56.4 (#20)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 1278 | 1467 |
| LMArena Chinese | 1295 | 1533 |
| LMArena French | 1258 | 1486 |
| LMArena German | 1262 | 1480 |
| LMArena Japanese | 1252 | 1498 |
| LMArena Korean | 1221 | 1460 |
| LMArena Russian | 1288 | 1473 |
| LMArena Spanish | 1243 | 1468 |
Instruction Following GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 66.8 (#205), GPT-5.5: 77.5 (#18)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 1258 | 1479 |
| IFEval | 83.1% | — |
Long Context GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 39.0 (#187), GPT-5.5: 48.3 (#12)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| LMArena Longer Query | 1284 | 1484 |
| CL-bench Life | — | 22.2% |
Writing & Preference GPT-5.5 leads
Gemini 1.5 Flash (May 2024): 48.7 (#196), GPT-5.5: 72.7 (#13)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.5 |
|---|---|---|
| LMArena Text | 1287 | 1472 |
| LMArena Creative Writing | 1285 | 1455 |
| LMArena Multi-Turn | 1253 | 1476 |
| EQ-Bench Creative Writing | — | 1844 |
| WildBench | 79.2% | — |
| EQ-Bench 4 | — | 1315 |
Frequently asked questions
Is Gemini 1.5 Flash (May 2024) better than GPT-5.5?
GPT-5.5 is the stronger model overall, scoring 63.4 to 33.2 on the Noometry Index.
Is Gemini 1.5 Flash (May 2024) or GPT-5.5 better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.2 versus 34.4 in the Noometry coding category.
How many benchmarks do Gemini 1.5 Flash (May 2024) and GPT-5.5 share?
25 benchmarks have published results for both models. Gemini 1.5 Flash (May 2024) has 42 scored results on Noometry and GPT-5.5 has 71.