Model comparison
Gemini 1.5 Flash (May 2024) vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 32.9 on the Noometry Index.
Last verified . 24 shared benchmarks.
GoogleSummary
- They share 24 benchmarks with published results for both. Gemini 1.5 Flash (May 2024) scores higher in 0 categories and GPT-5.6 Sol in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 20.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 16.3% for Gemini 1.5 Flash (May 2024) and 100% for GPT-5.6 Sol.
Side by side
| Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 32.9 | 64.4 |
| Released | 2024-05-14 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $4 |
| Output $ / M tokens | — | $20 |
| Results tracked | 42 | 64 |
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Category by category
Coding GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 33.9 (#233), GPT-5.6 Sol: 61.1 (#7)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| WeirdML | 24.9% | 89.4% |
| LMArena Coding | 1262 | 1493 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| LMArena WebDev | — | 1619 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| BigCodeBench Instruct | 43.5% | — |
| MirrorCode | — | 20% |
| BigCodeBench Complete | 55.1% | — |
| ALE-Bench | — | 2,177 |
| HumanEval+ | 75.6% | — |
| MBPP+ | 67.5% | — |
Agentic & Tool Use GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 26.3 (#97), GPT-5.6 Sol: 50.2 (#7)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| BALROG | 14.6% | 60% |
| APEX-Agents | — | 51.4% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |
Reasoning GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 20.8 (#212), GPT-5.6 Sol: 74.7 (#6)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1481 |
| DTBench | 53.8% | 96% |
| Epoch Capabilities Index | 129.37 | 161.99 |
| ARC-AGI-2 | — | 92.5% |
| SimpleBench | — | 71.7% |
| Kagi LLM Benchmark | — | 67% |
| NYT Connections (extended) | — | 93.8% |
| ARC-AGI-1 | — | 97.5% |
| CritPt | — | 32.3% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| Mystery Game Puzzles | — | 58% |
| LMCA | — | 59.2% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 53.9 | — |
| PIQA | 87.5% | — |
Math GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 20.8 (#274), GPT-5.6 Sol: 85.5 (#7)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.3% | 100% |
| LMArena Math | 1269 | 1472 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| ProofBench | — | 83% |
| Omni-MATH | 30.4% | — |
| MATH Level 5 | 61.9% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
| FrontierMath Erdős | — | 0% |
| GSM8K | 82.4% | — |
Knowledge GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 26.2 (#253), GPT-5.6 Sol: 64.3 (#16)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 47.3% | 93.5% |
| LMArena Expert | 1233 | 1517 |
| SimpleQA Verified | — | 69.7% |
| MMLU-Pro | 67.8% | — |
| Vectara Hallucination Rate | — | 12.4% |
| GPQA (HELM) | 43.7% | — |
| BoolQ | 85.8% | — |
| MMLU | 77.9% | — |
Multimodal GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 35.3 (#76), GPT-5.6 Sol: 48.9 (#8)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | 1141 | 1281 |
| Video-MME | 70.3% | — |
| GeoBench | 76% | — |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 43.1 (#184), GPT-5.6 Sol: 55.4 (#32)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1278 | 1450 |
| LMArena Chinese | 1295 | 1523 |
| LMArena French | 1257 | 1475 |
| LMArena German | 1262 | 1476 |
| LMArena Japanese | 1252 | 1475 |
| LMArena Korean | 1220 | 1434 |
| LMArena Russian | 1288 | 1466 |
| LMArena Spanish | 1242 | 1449 |
Instruction Following GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 67.0 (#199), GPT-5.6 Sol: 77.8 (#16)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1258 | 1480 |
| IFEval | 83.1% | — |
Long Context GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 39.0 (#182), GPT-5.6 Sol: 45.3 (#42)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1284 | 1479 |
Writing & Preference GPT-5.6 Sol leads
Gemini 1.5 Flash (May 2024): 48.9 (#190), GPT-5.6 Sol: 73.5 (#12)
| Benchmark | Gemini 1.5 Flash (May 2024) | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1287 | 1456 |
| LMArena Creative Writing | 1285 | 1450 |
| LMArena Multi-Turn | 1253 | 1456 |
| EQ-Bench Creative Writing | — | 1972 |
| WildBench | 79.2% | — |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is Gemini 1.5 Flash (May 2024) better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 32.9 on the Noometry Index.
Is Gemini 1.5 Flash (May 2024) or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 33.9 in the Noometry coding category.
How many benchmarks do Gemini 1.5 Flash (May 2024) and GPT-5.6 Sol share?
24 benchmarks have published results for both models. Gemini 1.5 Flash (May 2024) has 42 scored results on Noometry and GPT-5.6 Sol has 64.