Model comparison
LFM2.5-VL-450M vs Qwen3.7 Max
Head-to-head evidence from 4 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: LFM2.5-VL-450M unranked (Not scored); Qwen3.7 Max #10 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-450M and Qwen3.7 Max share 4 comparable benchmark results. 2 of 8 categories are comparable. 3 results are unique to LFM2.5-VL-450M; 54 to Qwen3.7 Max.
Updated July 23, 2026- Shared results
- 4
- LFM2.5-VL-450M only
- 3
- Qwen3.7 Max only
- 54
- Comparable categories
- 2 / 8
Treat this as a split decision. LFM2.5-VL-450M makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.7 Max is the better fit if knowledge is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 3 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
LFM2.5-VL-450M and Qwen3.7 Max finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Qwen3.7 Max is the reasoning model in the pair, while LFM2.5-VL-450M is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.7 Max gives you the larger context window at 1M, compared with 128K for LFM2.5-VL-450M.
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 | LFM2.5-VL-450M | Δ | Qwen3.7 Max |
|---|---|---|---|
| Knowledge | LFM2.5-VL-450M20.5 | Margin→ 43.7 | Qwen3.7 Max64.2 |
| Inst. Following | LFM2.5-VL-450M61.2 | Margin→ 23.2 | Qwen3.7 Max84.4 |
| Agentic | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.7 Max69.7 |
| Coding | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.7 Max77.9 |
| Reasoning | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.7 Max90.4 |
| Math | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.7 Max97.1 |
| Multilingual | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.7 Max87.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 19.3%B 89.6%Winner: Qwen3.7 MaxΔ 70.3MMLU-Pro: LFM2.5-VL-450M scored 19.3%; Qwen3.7 Max scored 89.6%. Qwen3.7 Max wins this benchmark. - Source ↗
GPQA
KnowledgeA 25.7%B 92.4%Winner: Qwen3.7 MaxΔ 66.7GPQA: LFM2.5-VL-450M scored 25.7%; Qwen3.7 Max scored 92.4%. Qwen3.7 Max wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 61.2%B 94.3%Winner: Qwen3.7 MaxΔ 33.1IFEval: LFM2.5-VL-450M scored 61.2%; Qwen3.7 Max scored 94.3%. Qwen3.7 Max wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-VL-450M | Qwen3.7 Max | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-450M$0 input / $0 output | Qwen3.7 MaxNot available | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-VL-450MNot available | Qwen3.7 MaxNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-450MNot available | Qwen3.7 MaxNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-450M128K | Qwen3.7 Max1M | Qwen3.7 Max lists the larger context window. |
Benchmark Deep Dive
Agentic21 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.7 Max | Result |
|---|---|---|---|
| BFCL v4Source | 21.1% | 75.0% | 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 |
| MCP AtlasSource | — | 76.4% | Not comparable |
| VITA-BenchSource | — | 47.9% | Not comparable |
| HLE w/ toolsSource | — | 53.5% | Not comparable |
| AA Agentic IndexSource | — | 30.6% | Not comparable |
| τ²-bench resultsSource | — | 94.7% | Not comparable |
| GDPval-AASource | — | 38.7% | Not comparable |
| GDPval-AASource | — | 1273 | 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 | LFM2.5-VL-450M | Qwen3.7 Max | Result |
|---|---|---|---|
| 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 |
| AA Coding IndexSource | — | 66.0% | Not comparable |
| AA-SciCodeSource | — | 48.8% | Not comparable |
Reasoning3 benchmarks
KnowledgeQwen3.7 Max wins13 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.7 Max | Result |
|---|---|---|---|
| GPQASource | 25.7% | 92.4% | Qwen3.7 Max leads |
| MMLU-ProSource | 19.3% | 89.6% | Qwen3.7 Max leads |
| GPQA-DSource | — | 92.4% | Not comparable |
| HLESource | — | 41.4% | Not comparable |
| MMLU-ReduxSource | — | 95% | Not comparable |
| SuperGPQASource | — | 73.6% | Not comparable |
| MMMLUSource | — | 90.3% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 46.0% | Not comparable |
| AA-GPQA DiamondSource | — | 92.3% | Not comparable |
| AA-HLESource | — | 38.1% | Not comparable |
| AA-Omniscience IndexSource | — | 14.1% | Not comparable |
| AA-Omniscience AccuracySource | — | 30.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 22.9% | Not comparable |
Math3 benchmarks
Multilingual5 benchmarks
Multimodal4 benchmarks
Frequently Asked Questions (3)
Which is better, LFM2.5-VL-450M or Qwen3.7 Max?
LFM2.5-VL-450M and Qwen3.7 Max are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, LFM2.5-VL-450M or Qwen3.7 Max?
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 64.2 versus 20.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Which is better for instruction following, LFM2.5-VL-450M or Qwen3.7 Max?
Qwen3.7 Max has the edge for instruction following in this comparison, averaging 84.4 versus 61.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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