# Gemini 2.5 Flash-Lite vs GPT-5.6 Sol

> GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 36.4 on the Noometry Index. Gemini 2.5 Flash-Lite costs 46× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemini-2-5-flash-lite-vs-gpt-5-6-sol
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. Gemini 2.5 Flash-Lite 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 reasoning, where GPT-5.6 Sol leads 74.7 to 21.5.
- The biggest single-benchmark swing is WeirdML: 35.2% for Gemini 2.5 Flash-Lite and 89.4% for GPT-5.6 Sol.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 1.05M.

## Snapshot

| | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| Provider | Google | OpenAI |
| Noometry Index | 36.4 | 64.4 |
| Rank | 208 | 6 |
| Context | 1.05M | 1.05M |
| Input $/M | $0.10 | $4 |
| Output $/M | $0.40 | $20 |
| Weights | Proprietary | Proprietary |

## Coding

- Gemini 2.5 Flash-Lite: 38.0 (#167)
- GPT-5.6 Sol: 61.1 (#7)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| WeirdML | 35.2% | 89.4% |
| LMArena Coding | 1373 | 1493 |
| ALE-Bench | 325.9 | 2,177 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| LMArena WebDev | — | 1619 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| MirrorCode | — | 20% |

## Agentic & Tool Use

- Gemini 2.5 Flash-Lite: 27.8 (#89)
- GPT-5.6 Sol: 50.2 (#7)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | — | 51.4% |
| Berkeley Function Calling Leaderboard | 36.9% | — |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |

## Reasoning

- Gemini 2.5 Flash-Lite: 21.5 (#196)
- GPT-5.6 Sol: 74.7 (#6)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| Kagi LLM Benchmark | 40.5% | 67% |
| LMArena Hard Prompts | 1377 | 1481 |
| DTBench | 62.8% | 96% |
| LMCA | 18.1% | 59.2% |
| Epoch Capabilities Index | 133.93 | 161.99 |
| ARC-AGI-2 | — | 92.5% |
| SimpleBench | — | 71.7% |
| 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% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |

## Math

- Gemini 2.5 Flash-Lite: 35.5 (#144)
- GPT-5.6 Sol: 85.5 (#7)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| LMArena Math | 1373 | 1472 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 83% |
| Omni-MATH | 48% | — |
| FrontierMath Erdős | — | 0% |

## Knowledge

- Gemini 2.5 Flash-Lite: 32.1 (#206)
- GPT-5.6 Sol: 64.3 (#16)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| Vectara Hallucination Rate | 3.3% | 12.4% |
| LMArena Expert | 1373 | 1517 |
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
| MMLU-Pro | 53.7% | — |
| GPQA (HELM) | 30.9% | — |

## Multimodal

- Gemini 2.5 Flash-Lite: 28.6 (#109)
- GPT-5.6 Sol: 48.9 (#8)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | 1198 | 1281 |
| VPCT | 30% | — |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |

## Multilingual

- Gemini 2.5 Flash-Lite: 49.5 (#129)
- GPT-5.6 Sol: 55.4 (#32)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1369 | 1450 |
| LMArena Chinese | 1404 | 1523 |
| LMArena French | 1388 | 1475 |
| LMArena German | 1390 | 1476 |
| LMArena Japanese | 1359 | 1475 |
| LMArena Korean | 1358 | 1434 |
| LMArena Russian | 1373 | 1466 |
| LMArena Spanish | 1396 | 1449 |

## Instruction Following

- Gemini 2.5 Flash-Lite: 70.2 (#163)
- GPT-5.6 Sol: 77.8 (#16)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1366 | 1480 |
| IFEval | 81% | — |

## Long Context

- Gemini 2.5 Flash-Lite: 33.3 (#257)
- GPT-5.6 Sol: 45.3 (#42)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1373 | 1479 |
| Fiction.LiveBench | 47.2% | — |

## Writing & Preference

- Gemini 2.5 Flash-Lite: 57.0 (#130)
- GPT-5.6 Sol: 73.5 (#12)

| Benchmark | Gemini 2.5 Flash-Lite | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1379 | 1456 |
| LMArena Creative Writing | 1366 | 1450 |
| LMArena Multi-Turn | 1367 | 1456 |
| EQ-Bench Creative Writing | — | 1972 |
| WildBench | 81.8% | — |
| EQ-Bench 4 | — | 1250 |

## FAQ

### Is Gemini 2.5 Flash-Lite better than GPT-5.6 Sol?

GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 36.4 on the Noometry Index. Gemini 2.5 Flash-Lite costs 46× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

### Which is cheaper, Gemini 2.5 Flash-Lite or GPT-5.6 Sol?

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 Sol lists at $4 and $20.

### Is Gemini 2.5 Flash-Lite or GPT-5.6 Sol better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 38.0 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 1.05M.

### How many benchmarks do Gemini 2.5 Flash-Lite and GPT-5.6 Sol share?

25 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and GPT-5.6 Sol has 64.