Model comparison
Llama 4 Maverick vs Qwen3.5-9B
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 30.9 on the Noometry Index.
Last verified . 7 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 7 benchmarks with published results for both. Llama 4 Maverick scores higher in 1 category and Qwen3.5-9B in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Llama 4 Maverick leads 28.2 to 14.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 20.6% for Llama 4 Maverick and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.19 / $0.65 for Llama 4 Maverick.
- Qwen3.5-9B accepts more context: 262K tokens versus 128K.
Side by side
| Llama 4 Maverick | Qwen3.5-9B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.9 | 33.8 |
| Released | 2025-04-05 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.19 | $0.10 |
| Output $ / M tokens | $0.65 | $0.15 |
| Results tracked | 54 | 10 |
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Category by category
Coding Qwen3.5-9B leads
Llama 4 Maverick: 26.6 (#324), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| SciCode | 33.1% | 27.5% |
| SWE-bench Verified (bash only) | 21% | — |
| Aider Polyglot | 15.6% | — |
| WeirdML | 24.5% | — |
| BigCodeBench Instruct | 49.7% | — |
| LMArena Coding | 1302 | — |
| BigCodeBench Complete | 61.4% | — |
| ALE-Bench | 172.97 | — |
Agentic & Tool Use Llama 4 Maverick leads
Llama 4 Maverick: 28.2 (#91), Qwen3.5-9B: 14.5 (#151)
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| Berkeley Function Calling Leaderboard | 37.3% | — |
Reasoning Qwen3.5-9B leads
Llama 4 Maverick: 10.1 (#342), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| CritPt | 0% | 0.3% |
| DTBench | 61.9% | 71.2% |
| LMCA | 15.9% | 24.5% |
| Epoch Capabilities Index | 132.2 | 139.46 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 27.7% | — |
| Kagi LLM Benchmark | 55.9% | — |
| NYT Connections (extended) | 8% | — |
| ARC-AGI-1 | 4.4% | — |
| Chess Puzzles | — | 12% |
| EnigmaEval | 0.6% | — |
| LMArena Hard Prompts | 1281 | — |
| ForecastBench | 57.5 | — |
Math Qwen3.5-9B leads
Llama 4 Maverick: 26.0 (#262), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 20.6% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
| Omni-MATH | 42.2% | — |
| LMArena Math | 1299 | — |
| MATH Level 5 | 73% | — |
| FrontierMath (Feb 2025 set) | 0.7% | — |
Knowledge Qwen3.5-9B leads
Llama 4 Maverick: 33.4 (#204), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 67% | 79% |
| Humanity's Last Exam | 5.7% | — |
| MMLU-Pro | 81% | — |
| Confabulations | 22.6% | — |
| Vectara Hallucination Rate | 8.2% | — |
| GPQA (HELM) | 65% | — |
| LMArena Expert | 1259 | — |
Multimodal Not comparable
Llama 4 Maverick: 31.6 (#105), Qwen3.5-9B: —
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| LMArena Vision | 1142 | — |
| GeoBench | 52% | — |
| SpatialViz-Bench | 31.8% | — |
Multilingual Not comparable
Llama 4 Maverick: 42.2 (#195), Qwen3.5-9B: —
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1269 | — |
| LMArena Chinese | 1277 | — |
| LMArena French | 1259 | — |
| LMArena German | 1291 | — |
| LMArena Japanese | 1207 | — |
| LMArena Korean | 1203 | — |
| LMArena Russian | 1286 | — |
| LMArena Spanish | 1293 | — |
Instruction Following Not comparable
Llama 4 Maverick: 71.7 (#146), Qwen3.5-9B: —
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| IFEval | 90.8% | — |
| LMArena Instruction Following | 1267 | — |
Long Context Not comparable
Llama 4 Maverick: 31.4 (#279), Qwen3.5-9B: —
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 46.2% | — |
| LMArena Longer Query | 1280 | — |
Writing & Preference Not comparable
Llama 4 Maverick: 38.8 (#252), Qwen3.5-9B: —
| Benchmark | Llama 4 Maverick | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1287 | — |
| LMArena Creative Writing | 1267 | — |
| Short-Story Creative Writing | 62% | — |
| EQ-Bench Creative Writing | 860 | — |
| WildBench | 80% | — |
| LMArena Multi-Turn | 1289 | — |
Frequently asked questions
Is Llama 4 Maverick better than Qwen3.5-9B?
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 30.9 on the Noometry Index.
Which is cheaper, Llama 4 Maverick or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Llama 4 Maverick lists at $0.19 and $0.65.
Is Llama 4 Maverick or Qwen3.5-9B better for coding?
Qwen3.5-9B scores higher on coding benchmarks: 35.9 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 128K.
How many benchmarks do Llama 4 Maverick and Qwen3.5-9B share?
7 benchmarks have published results for both models. Llama 4 Maverick has 54 scored results on Noometry and Qwen3.5-9B has 10.