Model comparison
LFM2.5-VL-450M vs Qwen3.6-35B-A3B
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: LFM2.5-VL-450M unranked; Qwen3.6-35B-A3B #31
BenchAlign evidence: LFM2.5-VL-450M not scored; Qwen3.6-35B-A3B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-450M and Qwen3.6-35B-A3B share 4 comparable benchmark results. 1 of 8 categories are comparable. 3 results are unique to LFM2.5-VL-450M; 54 to Qwen3.6-35B-A3B.
Updated July 15, 2026- Shared results
- 4
- LFM2.5-VL-450M only
- 3
- Qwen3.6-35B-A3B only
- 54
- Comparable categories
- 1 / 8
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.6-35B-A3B is clearly ahead on the provisional aggregate, 59 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-35B-A3B's sharpest advantage is in knowledge, where it averages 51.8 against 20.5. The single biggest benchmark swing on the page is MMLU-Pro, 19.3% to 85.2%.
Qwen3.6-35B-A3B 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. Qwen3.6-35B-A3B gives you the larger context window at 262K, 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 | Qwen3.6-35B-A3B |
|---|---|---|---|
| Knowledge | LFM2.5-VL-450M20.5 | Margin→ 31.3 | Qwen3.6-35B-A3B51.8 |
| Agentic | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-35B-A3B51.5 |
| Coding | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-35B-A3B73.8 |
| Math | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-35B-A3B88.2 |
| Multimodal | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-35B-A3B76.3 |
| Inst. Following | LFM2.5-VL-450M61.2 | MarginNo overlap | Qwen3.6-35B-A3BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 19.3%B 85.2%Winner: Qwen3.6-35B-A3BΔ 65.9MMLU-Pro: LFM2.5-VL-450M scored 19.3%; Qwen3.6-35B-A3B scored 85.2%. Qwen3.6-35B-A3B wins this benchmark. - Source ↗
GPQA
KnowledgeA 25.7%B 86%Winner: Qwen3.6-35B-A3BΔ 60.3GPQA: LFM2.5-VL-450M scored 25.7%; Qwen3.6-35B-A3B scored 86%. Qwen3.6-35B-A3B 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 | Qwen3.6-35B-A3B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-450M$0 input / $0 output | Qwen3.6-35B-A3BNot available | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-VL-450MNot available | Qwen3.6-35B-A3BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-450MNot available | Qwen3.6-35B-A3BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-450M128K | Qwen3.6-35B-A3B262K | Qwen3.6-35B-A3B lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| BFCL v4Source | 21.1% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 51.5% | Not comparable |
| Claw-EvalSource | — | 68.7% | Not comparable |
| QwenClawBenchSource | — | 52.6% | Not comparable |
| QwenWebBenchSource | — | 1397 | Not comparable |
| τ³-bench resultsSource | — | 67.2% | Not comparable |
| VITA-BenchSource | — | 35.6% | Not comparable |
| DeepPlanningSource | — | 25.9% | Not comparable |
| ToolathlonSource | — | 26.9% | Not comparable |
| MCP AtlasSource | — | 62.8% | Not comparable |
| WideResearchSource | — | 60.1% | Not comparable |
| AA Agentic IndexSource | — | 21.4% | Not comparable |
| τ²-bench resultsSource | — | 95.3% | Not comparable |
| GDPval-AASource | — | 27.4% | Not comparable |
| GDPval-AASource | — | 1049 | Not comparable |
| Gert LabsSource | — | 42.65% | Not comparable |
Coding9 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 73.4% | Not comparable |
| SWE MultilingualSource | — | 67.2% | Not comparable |
| SWE-bench ProSource | — | 49.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 51.5% | Not comparable |
| LiveCodeBenchSource | — | 80.4% | Not comparable |
| NL2RepoSource | — | 29.4% | Not comparable |
| AA Coding IndexSource | — | 41.9% | Not comparable |
| Terminal-Bench HardSource | — | 34.8% | Not comparable |
| AA-SciCodeSource | — | 35.8% | Not comparable |
Reasoning2 benchmarks
KnowledgeQwen3.6-35B-A3B wins11 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| GPQASource | 25.7% | 86% | Qwen3.6-35B-A3B leads |
| MMLU-ProSource | 19.3% | 85.2% | Qwen3.6-35B-A3B leads |
| SuperGPQASource | — | 64.7% | Not comparable |
| C-EvalSource | — | 90% | Not comparable |
| HLESource | — | 21.4% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 31.6% | Not comparable |
| AA-GPQA DiamondSource | — | 84.1% | Not comparable |
| AA-HLESource | — | 20.2% | Not comparable |
| AA-Omniscience IndexSource | — | -21.4% | Not comparable |
| AA-Omniscience AccuracySource | — | 18.9% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 49.7% | Not comparable |
Math5 benchmarks
Multimodal16 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-35B-A3B | Result |
|---|---|---|---|
| MMMUSource | 32.7% | 81.7% | Qwen3.6-35B-A3B leads |
| RealWorldQASource | 58.4% | 85.3% | Qwen3.6-35B-A3B leads |
| CountBenchSource | 73.3% | — | Not comparable |
| MMMU-ProSource | — | 75.3% | Not comparable |
| OmniDocBench 1.5Source | — | 89.9% | Not comparable |
| CharXivSource | — | 78% | Not comparable |
| SimpleVQASource | — | 58.9% | Not comparable |
| CC-OCRSource | — | 81.9% | Not comparable |
| AI2D_TESTSource | — | 92.7% | Not comparable |
| RefCOCO (avg)Source | — | 92.0% | Not comparable |
| ODINW13Source | — | 50.8% | Not comparable |
| Video-MME (with subtitle)Source | — | 86.6% | Not comparable |
| Video-MME (w/o subtitle)Source | — | 82.5% | Not comparable |
| VideoMMMUSource | — | 83.7% | Not comparable |
| MLVU (M-Avg)Source | — | 86.2% | Not comparable |
| AA-MMMU-ProSource | — | 75.0% | Not comparable |
Frequently Asked Questions (2)
Which is better, LFM2.5-VL-450M or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 59 to 34. The biggest single separator in this matchup is MMLU-Pro, where the scores are 19.3% and 85.2%.
Which is better for knowledge tasks, LFM2.5-VL-450M or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B has the edge for knowledge tasks in this comparison, averaging 51.8 versus 20.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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