Model comparison
Gemini 2.5 Flash-Lite vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 2.6× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
GoogleSummary
- They share 24 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 0 categories and GPT-5.6 Luna in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 38.0.
- The biggest single-benchmark swing is LMCA: 18.1% for Gemini 2.5 Flash-Lite and 48.5% for GPT-5.6 Luna.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 2.5 Flash-Lite | GPT-5.6 Luna | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 37.0 | 54.6 |
| Released | 2025-06-17 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.10 | $0.20 |
| Output $ / M tokens | $0.40 | $1.20 |
| Results tracked | 33 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 38.5 (#173), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| WeirdML | 35.2% | 60.9% |
| LMArena Coding | 1373 | 1466 |
| ALE-Bench | 325.9 | 1,667 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1519 |
| SciCode | — | 53.6% |
Agentic & Tool Use GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 28.0 (#96), GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | — | 43% |
| Berkeley Function Calling Leaderboard | 36.9% | — |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 22.2 (#205), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| Kagi LLM Benchmark | 40.5% | 49.1% |
| LMArena Hard Prompts | 1377 | 1451 |
| DTBench | 62.8% | 89.1% |
| LMCA | 18.1% | 48.5% |
| Epoch Capabilities Index | 133.94 | 156.39 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 21% |
| Surface Evolver Bench | — | 61.9% |
Math GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 38.0 (#144), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Math | 1373 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| OTIS Mock AIME 2024-2025 | — | 98.3% |
| ProofBench | — | 60% |
| Omni-MATH | 48% | — |
Knowledge GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 32.5 (#210), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Expert | 1373 | 1478 |
| GPQA Diamond | — | 91.6% |
| SimpleQA Verified | — | 41% |
| MMLU-Pro | 53.7% | — |
| Vectara Hallucination Rate | 3.3% | — |
| GPQA (HELM) | 30.9% | — |
Multimodal GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 29.1 (#114), GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | 1198 | 1258 |
| VPCT | 30% | — |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 49.3 (#134), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1369 | 1417 |
| LMArena Chinese | 1404 | 1470 |
| LMArena French | 1388 | 1456 |
| LMArena German | 1389 | 1454 |
| LMArena Japanese | 1359 | 1411 |
| LMArena Korean | 1360 | 1415 |
| LMArena Russian | 1373 | 1428 |
| LMArena Spanish | 1396 | 1448 |
Instruction Following GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 70.0 (#168), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1367 | 1437 |
| IFEval | 81% | — |
Long Context GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 33.3 (#262), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1373 | 1436 |
| Fiction.LiveBench | 47.2% | — |
Writing & Preference GPT-5.6 Luna leads
Gemini 2.5 Flash-Lite: 56.8 (#135), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1379 | 1431 |
| LMArena Creative Writing | 1367 | 1396 |
| LMArena Multi-Turn | 1366 | 1434 |
| EQ-Bench Creative Writing | — | 1829 |
| WildBench | 81.8% | — |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 2.6× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash-Lite or GPT-5.6 Luna?
Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is Gemini 2.5 Flash-Lite or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 38.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 2.5 Flash-Lite and GPT-5.6 Luna share?
24 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and GPT-5.6 Luna has 52.