Model comparison
o4-mini vs Qwen3-Next 80B-A3B Instruct
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 41.6 on the Noometry Index.
Last verified . 25 shared benchmarks.
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. o4-mini scores higher in 4 categories and Qwen3-Next 80B-A3B Instruct in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where o4-mini leads 45.5 to 37.0.
- The biggest single-benchmark swing is Omni-MATH: 72% for o4-mini 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 $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 131K.
- Qwen3-Next 80B-A3B Instruct has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 43.0 |
| Released | 2025-04-16 | 2025-09 |
| Weights | Proprietary | Open |
| Context window | 200K | 131K |
| Max output | 100K | 33K |
| Input $ / M tokens | $1.10 | $0.50 |
| Output $ / M tokens | $4.40 | $2 |
| Results tracked | 60 | 25 |
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Category by category
Coding Qwen3-Next 80B-A3B Instruct leads
o4-mini: 40.9 (#127), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1368 | 1440 |
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Not comparable
o4-mini: 32.6 (#61), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning Qwen3-Next 80B-A3B Instruct leads
o4-mini: 24.6 (#162), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 67.6% | 66.7% |
| LMArena Hard Prompts | 1351 | 1428 |
| 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% | — |
| DTBench | 77.6% | — |
| LMCA | 26.5% | — |
| Epoch Capabilities Index | 145.64 | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.8 (#89), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Omni-MATH | 72% | 46.7% |
| LMArena Math | 1389 | 1440 |
| 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
o4-mini: 43.6 (#91), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| MMLU-Pro | 82% | 78.6% |
| Vectara Hallucination Rate | 18.6% | 9.3% |
| GPQA (HELM) | 73.5% | 63% |
| LMArena Expert | 1343 | 1417 |
| GPQA Diamond | 79.6% | — |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| Confabulations | 15.8% | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Qwen3-Next 80B-A3B Instruct leads
o4-mini: 47.0 (#154), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1337 | 1407 |
| LMArena Chinese | 1354 | 1460 |
| LMArena French | 1364 | 1413 |
| LMArena German | 1336 | 1417 |
| LMArena Japanese | 1308 | 1395 |
| LMArena Korean | 1312 | 1364 |
| LMArena Russian | 1334 | 1404 |
| LMArena Spanish | 1347 | 1435 |
Instruction Following o4-mini leads
o4-mini: 75.2 (#68), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| IFEval | 92.8% | 81% |
| LMArena Instruction Following | 1321 | 1389 |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Fiction.LiveBench | 77.8% | 55.6% |
| LMArena Longer Query | 1315 | 1403 |
Writing & Preference Qwen3-Next 80B-A3B Instruct leads
o4-mini: 54.0 (#152), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)
| Benchmark | o4-mini | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1353 | 1417 |
| LMArena Creative Writing | 1294 | 1334 |
| WildBench | 85.4% | 80.7% |
| LMArena Multi-Turn | 1350 | 1416 |
| Short-Story Creative Writing | 75% | — |
Frequently asked questions
Is o4-mini better than Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 41.6 on the Noometry Index.
Which is cheaper, o4-mini 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; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3-Next 80B-A3B Instruct better for coding?
Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 131K.
How many benchmarks do o4-mini and Qwen3-Next 80B-A3B Instruct share?
25 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.