Model comparison
Gemini 2.5 Flash-Lite vs GPT-5.2
GPT-5.2 is the stronger model overall, scoring 54.1 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 27× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
GoogleSummary
- They share 27 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 0 categories and GPT-5.2 in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 22.2.
- The biggest single-benchmark swing is VPCT: 30% for Gemini 2.5 Flash-Lite and 84% for GPT-5.2.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 400K.
Side by side
| Gemini 2.5 Flash-Lite | GPT-5.2 | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 37.0 | 54.1 |
| Released | 2025-06-17 | 2025-12-11 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 400K |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.10 | $1.75 |
| Output $ / M tokens | $0.40 | $14 |
| Results tracked | 33 | 67 |
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Category by category
Coding GPT-5.2 leads
Gemini 2.5 Flash-Lite: 38.5 (#173), GPT-5.2: 51.6 (#37)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| WeirdML | 35.2% | 72.2% |
| LMArena Coding | 1373 | 1447 |
| ALE-Bench | 325.9 | 1,294 |
| SWE-bench Verified | — | 73.8% |
| SWE-bench Verified (bash only) | — | 72.8% |
| LMArena WebDev | — | 1416 |
| SWE-bench Multilingual | — | 66.7% |
| GSO | — | 27.4% |
| AlgoTune | — | 2.05 |
Agentic & Tool Use GPT-5.2 leads
Gemini 2.5 Flash-Lite: 28.0 (#96), GPT-5.2: 40.2 (#24)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | 55.9% |
| Terminal-Bench | — | 64.9% |
| GDPval | — | 49.7% |
| Remote Labor Index | — | 2.5% |
| τ²-bench Airline | — | 83% |
| τ²-bench Banking | — | 32.2% |
| τ²-bench Retail | — | 81.6% |
| τ²-bench Telecom | — | 89.7% |
| DeepResearch Bench | — | 41.1% |
| LMArena Search | — | 1207 |
| METR Time Horizons | — | 75.3% |
| Vending-Bench 2 | — | 3,591 |
Reasoning GPT-5.2 leads
Gemini 2.5 Flash-Lite: 22.2 (#205), GPT-5.2: 50.2 (#35)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| Kagi LLM Benchmark | 40.5% | 73.3% |
| LMArena Hard Prompts | 1377 | 1445 |
| DTBench | 62.8% | 90.9% |
| LMCA | 18.1% | 43.9% |
| Epoch Capabilities Index | 133.94 | 153.45 |
| ARC-AGI-2 | — | 52.9% |
| SimpleBench | — | 45.8% |
| NYT Connections (extended) | — | 83.6% |
| ARC-AGI-1 | — | 86.2% |
| Chess Puzzles | — | 49% |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| Mystery Game Puzzles | — | 23% |
| ForecastBench | — | 60.1 |
Math GPT-5.2 leads
Gemini 2.5 Flash-Lite: 38.0 (#144), GPT-5.2: 60.0 (#38)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| LMArena Math | 1373 | 1440 |
| FrontierMath (Tiers 1-3) | — | 67.4% |
| FrontierMath Tier 4 | — | 31.7% |
| MathArena Final-Answer Competitions | — | 72% |
| OTIS Mock AIME 2024-2025 | — | 96.1% |
| ProofBench | — | 15% |
| Omni-MATH | 48% | — |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
Knowledge GPT-5.2 leads
Gemini 2.5 Flash-Lite: 32.5 (#210), GPT-5.2: 59.3 (#32)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| Vectara Hallucination Rate | 3.3% | 8.4% |
| LMArena Expert | 1373 | 1445 |
| GPQA Diamond | — | 91.4% |
| Humanity's Last Exam | — | 27.8% |
| SimpleQA Verified | — | 37.1% |
| MMLU-Pro | 53.7% | — |
| GPQA (HELM) | 30.9% | — |
Multimodal GPT-5.2 leads
Gemini 2.5 Flash-Lite: 29.1 (#114), GPT-5.2: 51.3 (#7)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| LMArena Vision | 1198 | 1268 |
| VPCT | 30% | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual GPT-5.2 leads
Gemini 2.5 Flash-Lite: 49.3 (#134), GPT-5.2: 53.4 (#67)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1369 | 1425 |
| LMArena Chinese | 1404 | 1460 |
| LMArena French | 1388 | 1455 |
| LMArena German | 1389 | 1448 |
| LMArena Japanese | 1359 | 1420 |
| LMArena Korean | 1360 | 1392 |
| LMArena Russian | 1373 | 1440 |
| LMArena Spanish | 1396 | 1433 |
Instruction Following GPT-5.2 leads
Gemini 2.5 Flash-Lite: 70.0 (#168), GPT-5.2: 74.7 (#89)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1367 | 1417 |
| IFEval | 81% | — |
Long Context GPT-5.2 leads
Gemini 2.5 Flash-Lite: 33.3 (#262), GPT-5.2: 44.0 (#78)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1373 | 1428 |
| Fiction.LiveBench | 47.2% | — |
| CL-bench | — | 18.2% |
Writing & Preference GPT-5.2 leads
Gemini 2.5 Flash-Lite: 56.8 (#135), GPT-5.2: 66.8 (#32)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.2 |
|---|---|---|
| LMArena Text | 1379 | 1439 |
| LMArena Creative Writing | 1367 | 1401 |
| LMArena Multi-Turn | 1366 | 1458 |
| EQ-Bench Creative Writing | — | 1703 |
| WildBench | 81.8% | — |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than GPT-5.2?
GPT-5.2 is the stronger model overall, scoring 54.1 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 27× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash-Lite or GPT-5.2?
Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is Gemini 2.5 Flash-Lite or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 38.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 400K.
How many benchmarks do Gemini 2.5 Flash-Lite and GPT-5.2 share?
27 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and GPT-5.2 has 67.