Model comparison
Llama 4 Scout vs Qwen3.7 Max
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Llama 4 Scout #174 (Supported); Qwen3.7 Max #10 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Llama 4 Scout and Qwen3.7 Max share 16 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to Llama 4 Scout; 42 to Qwen3.7 Max.
Updated July 23, 2026- Shared results
- 16
- Llama 4 Scout only
- 2
- Qwen3.7 Max only
- 42
- Comparable categories
- 0 / 8
Benchmark data for Llama 4 Scout and Qwen3.7 Max is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Llama 4 Scout has the larger context window at 10M, compared with 1M for Qwen3.7 Max.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Llama 4 Scout | Δ | Qwen3.7 Max |
|---|---|---|---|
| Agentic | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max69.7 |
| Coding | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max77.9 |
| Reasoning | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max90.4 |
| Knowledge | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max64.2 |
| Math | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max97.1 |
| Multilingual | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max87.0 |
| Inst. Following | Llama 4 ScoutNot measured | MarginNo overlap | Qwen3.7 Max84.4 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Llama 4 Scout | Qwen3.7 Max | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Llama 4 Scout$0 input / $0 output | Qwen3.7 MaxNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Llama 4 Scout128 tok/s | Qwen3.7 MaxNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Llama 4 Scout0.70 s | Qwen3.7 MaxNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Llama 4 Scout10M | Qwen3.7 Max1M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic21 benchmarks
| Benchmark | Llama 4 Scout | Qwen3.7 Max | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.1% | 30.6% | Qwen3.7 Max leads |
| τ²-bench resultsSource | 15.5% | 94.7% | Qwen3.7 Max leads |
| GDPval-AASource | 0.0% | 38.7% | Qwen3.7 Max leads |
| GDPval-AASource | 90 | 1273 | Qwen3.7 Max leads |
| Terminal-Bench 2.0Source | — | 69.7% | Not comparable |
| QwenClawBenchSource | — | 64.3% | Not comparable |
| QwenWebBenchSource | — | 1568 | Not comparable |
| Claw-EvalSource | — | 65.2% | Not comparable |
| BFCL v4Source | — | 75.0% | Not comparable |
| MCP AtlasSource | — | 76.4% | Not comparable |
| VITA-BenchSource | — | 47.9% | Not comparable |
| HLE w/ toolsSource | — | 53.5% | Not comparable |
| Gert LabsSource | — | 64.27% | Not comparable |
| ResearchClawBenchSource | — | 18.7% | Not comparable |
| AA BriefcaseSource | — | 908 | Not comparable |
| AA AutomationBenchSource | — | 25.6% | Not comparable |
| AA EnterpriseOps-GymSource | — | 45.0% | Not comparable |
| AA ITBenchSource | — | 42.5% | Not comparable |
| terminalBenchHardSource | — | 50.8% | Not comparable |
| aaTerminalBench21Source | — | 74.5% | Not comparable |
| AA Harvey LABSource | — | 83.4% | Not comparable |
Coding9 benchmarks
| Benchmark | Llama 4 Scout | Qwen3.7 Max | Result |
|---|---|---|---|
| AA Coding IndexSource | 8.2% | 66.0% | Qwen3.7 Max leads |
| AA-SciCodeSource | 17.0% | 48.8% | Qwen3.7 Max leads |
| SWE-bench VerifiedSource | — | 80.4% | Not comparable |
| SWE-bench ProSource | — | 60.6% | Not comparable |
| SWE MultilingualSource | — | 78.3% | Not comparable |
| NL2RepoSource | — | 47.2% | Not comparable |
| SciCodeSource | — | 53.5% | Not comparable |
| LiveCodeBenchSource | — | 91.6% | Not comparable |
| Terminal-Bench 2.0Source | — | 69.7% | Not comparable |
Reasoning3 benchmarks
Knowledge13 benchmarks
| Benchmark | Llama 4 Scout | Qwen3.7 Max | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 10.0% | 46.0% | Qwen3.7 Max leads |
| AA-GPQA DiamondSource | 58.7% | 92.3% | Qwen3.7 Max leads |
| AA-HLESource | 4.3% | 38.1% | Qwen3.7 Max leads |
| AA-Omniscience IndexSource | -52.4% | 14.1% | Qwen3.7 Max leads |
| AA-Omniscience AccuracySource | 14.6% | 30.1% | Qwen3.7 Max leads |
| AA-Omniscience Hallucination RateSource | 78.3% | 22.9% | Qwen3.7 Max leads |
| GPQASource | — | 92.4% | Not comparable |
| GPQA-DSource | — | 92.4% | Not comparable |
| HLESource | — | 41.4% | Not comparable |
| MMLU-ProSource | — | 89.6% | Not comparable |
| MMLU-ReduxSource | — | 95% | Not comparable |
| SuperGPQASource | — | 73.6% | Not comparable |
| MMMLUSource | — | 90.3% | Not comparable |
Math4 benchmarks
Multilingual5 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (3)
Can I compare Llama 4 Scout and Qwen3.7 Max on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Llama 4 Scout and Qwen3.7 Max today?
Llama 4 Scout: $0.00 input / $0.00 output per 1M tokens Qwen3.7 Max: Pricing unavailable Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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