Model comparison
Llama 4 Maverick vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 30.9 on the Noometry Index.
Last verified . 25 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. Llama 4 Maverick scores higher in 0 categories and Qwen3.5-Flash in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5-Flash leads 33.7 to 10.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 20.6% for Llama 4 Maverick and 84.4% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.19 / $0.65 for Llama 4 Maverick.
- Qwen3.5-Flash accepts more context: 1M tokens versus 128K.
- Llama 4 Maverick has downloadable open weights; the other is API-only.
Side by side
| Llama 4 Maverick | Qwen3.5-Flash | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.9 | 42.5 |
| Released | 2025-04-05 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.19 | $0.10 |
| Output $ / M tokens | $0.65 | $0.40 |
| Results tracked | 54 | 32 |
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Category by category
Coding Qwen3.5-Flash leads
Llama 4 Maverick: 26.6 (#324), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1302 | 1412 |
| ALE-Bench | 172.97 | 221.8 |
| SWE-bench Verified (bash only) | 21% | — |
| Aider Polyglot | 15.6% | — |
| LMArena WebDev | — | 1244 |
| SciCode | 33.1% | — |
| WeirdML | 24.5% | — |
| BigCodeBench Instruct | 49.7% | — |
| BigCodeBench Complete | 61.4% | — |
Agentic & Tool Use Not comparable
Llama 4 Maverick: 28.2 (#91), Qwen3.5-Flash: —
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 37.3% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Llama 4 Maverick: 10.1 (#342), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1281 | 1403 |
| DTBench | 61.9% | 82.9% |
| LMCA | 15.9% | 29.1% |
| Epoch Capabilities Index | 132.2 | 143.98 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 27.7% | — |
| Kagi LLM Benchmark | 55.9% | — |
| NYT Connections (extended) | 8% | — |
| ARC-AGI-1 | 4.4% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 21% |
| EnigmaEval | 0.6% | — |
| Mystery Game Puzzles | — | 20% |
| ForecastBench | 57.5 | — |
Math Qwen3.5-Flash leads
Llama 4 Maverick: 26.0 (#262), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 20.6% | 84.4% |
| LMArena Math | 1299 | 1407 |
| FrontierMath (Feb 2025 set) | 0.7% | 6.2% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 42.2% | — |
| MATH Level 5 | 73% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
Llama 4 Maverick: 33.4 (#204), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 67% | 82.3% |
| Vectara Hallucination Rate | 8.2% | 10.5% |
| LMArena Expert | 1259 | 1407 |
| Humanity's Last Exam | 5.7% | — |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 81% | — |
| Confabulations | 22.6% | — |
| GPQA (HELM) | 65% | — |
Multimodal Not comparable
Llama 4 Maverick: 31.6 (#105), Qwen3.5-Flash: —
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1142 | — |
| GeoBench | 52% | — |
| SpatialViz-Bench | 31.8% | — |
Multilingual Qwen3.5-Flash leads
Llama 4 Maverick: 42.2 (#195), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1269 | 1385 |
| LMArena Chinese | 1277 | 1446 |
| LMArena French | 1259 | 1412 |
| LMArena German | 1291 | 1390 |
| LMArena Japanese | 1207 | 1368 |
| LMArena Korean | 1203 | 1344 |
| LMArena Russian | 1286 | 1379 |
| LMArena Spanish | 1293 | 1400 |
Instruction Following Too close to call
Llama 4 Maverick: 71.7 (#146), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1267 | 1374 |
| IFEval | 90.8% | — |
Long Context Qwen3.5-Flash leads
Llama 4 Maverick: 31.4 (#279), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1280 | 1392 |
| Fiction.LiveBench | 46.2% | — |
Writing & Preference Qwen3.5-Flash leads
Llama 4 Maverick: 38.8 (#252), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | Llama 4 Maverick | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1287 | 1397 |
| LMArena Creative Writing | 1267 | 1343 |
| LMArena Multi-Turn | 1289 | 1393 |
| Short-Story Creative Writing | 62% | — |
| EQ-Bench Creative Writing | 860 | — |
| WildBench | 80% | — |
Frequently asked questions
Is Llama 4 Maverick better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 30.9 on the Noometry Index.
Which is cheaper, Llama 4 Maverick or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Llama 4 Maverick lists at $0.19 and $0.65.
Is Llama 4 Maverick or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 34.2 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 128K.
How many benchmarks do Llama 4 Maverick and Qwen3.5-Flash share?
25 benchmarks have published results for both models. Llama 4 Maverick has 54 scored results on Noometry and Qwen3.5-Flash has 32.