Model comparison
Gemini 2.5 Flash-Lite vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 11× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
GoogleSummary
- They share 33 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 2 categories and o4-mini in 8 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in long context, where o4-mini leads 45.5 to 33.3.
- The biggest single-benchmark swing is GPQA (HELM): 30.9% for Gemini 2.5 Flash-Lite and 73.5% for o4-mini.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 200K.
Side by side
| Gemini 2.5 Flash-Lite | o4-mini | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 37.0 | 41.6 |
| Released | 2025-06-17 | 2025-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 66K | 100K |
| Input $ / M tokens | $0.10 | $1.10 |
| Output $ / M tokens | $0.40 | $4.40 |
| Results tracked | 33 | 60 |
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Category by category
Coding o4-mini leads
Gemini 2.5 Flash-Lite: 38.5 (#173), o4-mini: 40.9 (#127)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| WeirdML | 35.2% | 52.6% |
| LMArena Coding | 1373 | 1368 |
| ALE-Bench | 325.9 | 826.17 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| CadEval | — | 62% |
| AlgoTune | — | 1.72 |
Agentic & Tool Use o4-mini leads
Gemini 2.5 Flash-Lite: 28.0 (#96), o4-mini: 32.6 (#61)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Gemini 2.5 Flash-Lite: 22.2 (#205), o4-mini: 24.6 (#162)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| Kagi LLM Benchmark | 40.5% | 67.6% |
| LMArena Hard Prompts | 1377 | 1351 |
| DTBench | 62.8% | 77.6% |
| LMCA | 18.1% | 26.5% |
| Epoch Capabilities Index | 133.94 | 145.64 |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| ForecastBench | — | 61.8 |
Math o4-mini leads
Gemini 2.5 Flash-Lite: 38.0 (#144), o4-mini: 40.8 (#89)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| Omni-MATH | 48% | 72% |
| LMArena Math | 1373 | 1389 |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
Gemini 2.5 Flash-Lite: 32.5 (#210), o4-mini: 43.6 (#91)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| MMLU-Pro | 53.7% | 82% |
| Vectara Hallucination Rate | 3.3% | 18.6% |
| GPQA (HELM) | 30.9% | 73.5% |
| LMArena Expert | 1373 | 1343 |
| GPQA Diamond | — | 79.6% |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| Confabulations | — | 15.8% |
Multimodal o4-mini leads
Gemini 2.5 Flash-Lite: 29.1 (#114), o4-mini: 40.2 (#49)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| LMArena Vision | 1198 | 1194 |
| VPCT | 30% | 57.5% |
| GeoBench | — | 64% |
Multilingual Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 49.3 (#134), o4-mini: 47.0 (#154)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| LMArena Non-English | 1369 | 1337 |
| LMArena Chinese | 1404 | 1354 |
| LMArena French | 1388 | 1364 |
| LMArena German | 1389 | 1336 |
| LMArena Japanese | 1359 | 1308 |
| LMArena Korean | 1360 | 1312 |
| LMArena Russian | 1373 | 1334 |
| LMArena Spanish | 1396 | 1347 |
Instruction Following o4-mini leads
Gemini 2.5 Flash-Lite: 70.0 (#168), o4-mini: 75.2 (#68)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| IFEval | 81% | 92.8% |
| LMArena Instruction Following | 1367 | 1321 |
Long Context o4-mini leads
Gemini 2.5 Flash-Lite: 33.3 (#262), o4-mini: 45.5 (#33)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| Fiction.LiveBench | 47.2% | 77.8% |
| LMArena Longer Query | 1373 | 1315 |
Writing & Preference Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 56.8 (#135), o4-mini: 54.0 (#152)
| Benchmark | Gemini 2.5 Flash-Lite | o4-mini |
|---|---|---|
| LMArena Text | 1379 | 1353 |
| LMArena Creative Writing | 1367 | 1294 |
| WildBench | 81.8% | 85.4% |
| LMArena Multi-Turn | 1366 | 1350 |
| Short-Story Creative Writing | — | 75% |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 11× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash-Lite or o4-mini?
Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is Gemini 2.5 Flash-Lite or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 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 200K.
How many benchmarks do Gemini 2.5 Flash-Lite and o4-mini share?
33 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and o4-mini has 60.