# GPT-4o vs Qwen3-Next 80B-A3B Instruct

> Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 28.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-4o-vs-qwen3-next-80b-a3b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GPT-4o scores higher in 1 category and Qwen3-Next 80B-A3B Instruct in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3-Next 80B-A3B Instruct leads 38.8 to 10.6.
- The biggest single-benchmark swing is Omni-MATH: 29.3% for GPT-4o and 46.7% for Qwen3-Next 80B-A3B Instruct.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Qwen3-Next 80B-A3B Instruct accepts more context: 131K tokens versus 128K.
- Qwen3-Next 80B-A3B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 28.6 | 43.0 |
| Rank | 324 | 102 |
| Context | 128K | 131K |
| Input $/M | $2.50 | $0.50 |
| Output $/M | $10 | $2 |
| Weights | Proprietary | Open |

## Coding

- GPT-4o: 24.8 (#328)
- Qwen3-Next 80B-A3B Instruct: 42.5 (#98)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1297 | 1440 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |

## Agentic & Tool Use

- GPT-4o: 21.0 (#141)
- Qwen3-Next 80B-A3B Instruct: —

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |

## Reasoning

- GPT-4o: 9.4 (#343)
- Qwen3-Next 80B-A3B Instruct: 31.1 (#81)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1281 | 1428 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| Kagi LLM Benchmark | — | 66.7% |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| DTBench | 64.5% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 16.6% | — |
| Epoch Capabilities Index | 128.97 | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |

## Math

- GPT-4o: 10.6 (#312)
- Qwen3-Next 80B-A3B Instruct: 38.8 (#126)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Omni-MATH | 29.3% | 46.7% |
| LMArena Math | 1285 | 1440 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |

## Knowledge

- GPT-4o: 28.8 (#242)
- Qwen3-Next 80B-A3B Instruct: 41.8 (#106)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| MMLU-Pro | 71.3% | 78.6% |
| Vectara Hallucination Rate | 9.6% | 9.3% |
| GPQA (HELM) | 52% | 63% |
| LMArena Expert | 1250 | 1417 |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| Confabulations | 15.3% | — |
| MMLU | 88.1% | — |

## Multimodal

- GPT-4o: 34.5 (#91)
- Qwen3-Next 80B-A3B Instruct: —

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |

## Multilingual

- GPT-4o: 43.2 (#186)
- Qwen3-Next 80B-A3B Instruct: 52.1 (#93)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1283 | 1407 |
| LMArena Chinese | 1277 | 1460 |
| LMArena French | 1304 | 1413 |
| LMArena German | 1282 | 1417 |
| LMArena Japanese | 1257 | 1395 |
| LMArena Korean | 1234 | 1364 |
| LMArena Russian | 1286 | 1404 |
| LMArena Spanish | 1292 | 1435 |

## Instruction Following

- GPT-4o: 66.6 (#207)
- Qwen3-Next 80B-A3B Instruct: 70.8 (#159)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| IFEval | 81.7% | 81% |
| LMArena Instruction Following | 1278 | 1389 |
| LiveBench Instruction Following | 68.6% | — |

## Long Context

- GPT-4o: 39.4 (#179)
- Qwen3-Next 80B-A3B Instruct: 37.0 (#223)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Fiction.LiveBench | 66.7% | 55.6% |
| LMArena Longer Query | 1289 | 1403 |

## Writing & Preference

- GPT-4o: 52.6 (#166)
- Qwen3-Next 80B-A3B Instruct: 58.0 (#121)

| Benchmark | GPT-4o | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1300 | 1417 |
| LMArena Creative Writing | 1292 | 1334 |
| WildBench | 82.8% | 80.7% |
| LMArena Multi-Turn | 1302 | 1416 |
| Short-Story Creative Writing | 81.8% | — |
| LiveBench Language | 47.6% | — |

## FAQ

### Is GPT-4o better than Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 28.6 on the Noometry Index.

### Which is cheaper, GPT-4o or Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is cheaper. It lists at $0.50 per million input tokens and $2 per million output tokens; GPT-4o lists at $2.50 and $10.

### Is GPT-4o or Qwen3-Next 80B-A3B Instruct better for coding?

Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 24.8 in the Noometry coding category.

### Which has the bigger context window?

Qwen3-Next 80B-A3B Instruct does, with 131K tokens against 128K.

### How many benchmarks do GPT-4o and Qwen3-Next 80B-A3B Instruct share?

24 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.