Model comparison
Gemini 2.5 Flash vs GPT-5.3 Chat
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 39.3 on the Noometry Index. Gemini 2.5 Flash costs 5.7× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
GoogleSummary
- They share 18 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 4 categories and GPT-5.3 Chat in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.3 Chat leads 28.5 to 18.1.
- Gemini 2.5 Flash is cheaper at $0.30 / $2.50 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 128K.
Side by side
| Gemini 2.5 Flash | GPT-5.3 Chat | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 39.3 | 42.8 |
| Released | 2025-04-17 | 2026-03-03 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 128K |
| Max output | 66K | 16K |
| Input $ / M tokens | $0.30 | $1.75 |
| Output $ / M tokens | $2.50 | $14 |
| Results tracked | 54 | 18 |
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Category by category
Coding GPT-5.3 Chat leads
Gemini 2.5 Flash: 35.8 (#220), GPT-5.3 Chat: 41.4 (#124)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Coding | 1424 | 1408 |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| WeirdML | 41.9% | — |
| ALE-Bench | 661.88 | — |
Agentic & Tool Use Not comparable
Gemini 2.5 Flash: 30.8 (#74), GPT-5.3 Chat: —
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| Terminal-Bench | 17.1% | — |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| Vending-Bench 2 | 548.84 | — |
Reasoning GPT-5.3 Chat leads
Gemini 2.5 Flash: 18.1 (#286), GPT-5.3 Chat: 28.5 (#102)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1399 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| CritPt | 1.1% | — |
| EnigmaEval | 2.7% | — |
| DTBench | 76.5% | — |
| LMCA | 27.5% | — |
| Epoch Capabilities Index | 143.03 | — |
| ForecastBench | 60.6 | — |
Math Gemini 2.5 Flash leads
Gemini 2.5 Flash: 39.9 (#98), GPT-5.3 Chat: 38.2 (#142)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Math | 1415 | 1389 |
| OTIS Mock AIME 2024-2025 | 73.1% | — |
| Omni-MATH | 38.5% | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GPT-5.3 Chat leads
Gemini 2.5 Flash: 36.4 (#168), GPT-5.3 Chat: 38.8 (#140)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Expert | 1426 | 1397 |
| Humanity's Last Exam | 12.1% | — |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |
Multimodal Not comparable
Gemini 2.5 Flash: 41.8 (#32), GPT-5.3 Chat: —
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Vision | 1253 | — |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| SpatialViz-Bench | 36.9% | — |
Multilingual Gemini 2.5 Flash leads
Gemini 2.5 Flash: 52.3 (#88), GPT-5.3 Chat: 50.3 (#124)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | 1409 | 1382 |
| LMArena Chinese | 1450 | 1432 |
| LMArena French | 1433 | 1397 |
| LMArena German | 1418 | 1384 |
| LMArena Japanese | 1405 | 1352 |
| LMArena Korean | 1385 | 1346 |
| LMArena Russian | 1415 | 1400 |
| LMArena Spanish | 1421 | 1371 |
Instruction Following Gemini 2.5 Flash leads
Gemini 2.5 Flash: 75.7 (#54), GPT-5.3 Chat: 72.8 (#129)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | 1405 | 1378 |
| IFEval | 89.8% | — |
Long Context Gemini 2.5 Flash leads
Gemini 2.5 Flash: 47.5 (#17), GPT-5.3 Chat: 42.6 (#120)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | 1419 | 1396 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference GPT-5.3 Chat leads
Gemini 2.5 Flash: 53.8 (#157), GPT-5.3 Chat: 63.1 (#68)
| Benchmark | Gemini 2.5 Flash | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | 1417 | 1389 |
| LMArena Creative Writing | 1400 | 1355 |
| EQ-Bench Creative Writing | 1137 | 1690 |
| LMArena Multi-Turn | 1408 | 1412 |
| Short-Story Creative Writing | 76.5% | — |
| WildBench | 81.7% | — |
Frequently asked questions
Is Gemini 2.5 Flash better than GPT-5.3 Chat?
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 39.3 on the Noometry Index. Gemini 2.5 Flash costs 5.7× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash or GPT-5.3 Chat?
Gemini 2.5 Flash is cheaper. It lists at $0.30 per million input tokens and $2.50 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is Gemini 2.5 Flash or GPT-5.3 Chat better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 41.4 versus 35.8 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash does, with 1.05M tokens against 128K.
How many benchmarks do Gemini 2.5 Flash and GPT-5.3 Chat share?
18 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and GPT-5.3 Chat has 18.