Model comparison
LFM2.5-VL-450M vs Qwen3.6-27B
Head-to-head evidence from 5 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: LFM2.5-VL-450M unranked (Not scored); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-450M and Qwen3.6-27B share 5 comparable benchmark results. 1 of 8 categories are comparable. 2 results are unique to LFM2.5-VL-450M; 49 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 5
- LFM2.5-VL-450M only
- 2
- Qwen3.6-27B only
- 49
- Comparable categories
- 1 / 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; Qwen3.6-27B is the better fit if knowledge is the priority or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 5 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
LFM2.5-VL-450M and Qwen3.6-27B 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.
Qwen3.6-27B 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. Qwen3.6-27B 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 | Qwen3.6-27B |
|---|---|---|---|
| Knowledge | LFM2.5-VL-450M20.5 | Margin→ 32.8 | Qwen3.6-27B53.3 |
| Agentic | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-27B59.3 |
| Coding | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-27B77.5 |
| Math | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-27B89.2 |
| Multimodal | LFM2.5-VL-450MNot measured | MarginNo overlap | Qwen3.6-27B76.7 |
| Inst. Following | LFM2.5-VL-450M61.2 | MarginNo overlap | Qwen3.6-27BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 19.3%B 86.2%Winner: Qwen3.6-27BΔ 66.9MMLU-Pro: LFM2.5-VL-450M scored 19.3%; Qwen3.6-27B scored 86.2%. Qwen3.6-27B wins this benchmark. - Source ↗
GPQA
KnowledgeA 25.7%B 87.8%Winner: Qwen3.6-27BΔ 62.1GPQA: LFM2.5-VL-450M scored 25.7%; Qwen3.6-27B scored 87.8%. Qwen3.6-27B 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 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-450M$0 input / $0 output | Qwen3.6-27B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | LFM2.5-VL-450MNot available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-450MNot available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-450M128K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-27B | Result |
|---|---|---|---|
| BFCL v4Source | 21.1% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
| AA Agentic IndexSource | — | 27.0% | Not comparable |
| τ²-bench resultsSource | — | 94.2% | Not comparable |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
| Gert LabsSource | — | 54.84% | Not comparable |
Coding8 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| SWE-bench ProSource | — | 53.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
| AA Coding IndexSource | — | 53.7% | Not comparable |
| AA-SciCodeSource | — | 39.8% | Not comparable |
Reasoning2 benchmarks
KnowledgeQwen3.6-27B wins12 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQASource | 25.7% | 87.8% | Qwen3.6-27B leads |
| MMLU-ProSource | 19.3% | 86.2% | Qwen3.6-27B leads |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
| HLESource | — | 24% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 37.0% | Not comparable |
| AA-GPQA DiamondSource | — | 84.2% | Not comparable |
| AA-HLESource | — | 21.6% | Not comparable |
| AA-Omniscience IndexSource | — | -19.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 19.2% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 48.3% | Not comparable |
Math5 benchmarks
Multimodal16 benchmarks
| Benchmark | LFM2.5-VL-450M | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMUSource | 32.7% | 82.9% | Qwen3.6-27B leads |
| RealWorldQASource | 58.4% | 84.1% | Qwen3.6-27B leads |
| CountBenchSource | 73.3% | 97.8% | Qwen3.6-27B leads |
| MMMU-ProSource | — | 75.8% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| SimpleVQASource | — | 56.1% | Not comparable |
| CharXivSource | — | 78.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | Not comparable |
| ERQASource | — | 62.5% | Not comparable |
| Video-MME (with subtitle)Source | — | 87.7% | Not comparable |
| VideoMMMUSource | — | 84.4% | Not comparable |
| MLVU (M-Avg)Source | — | 86.6% | Not comparable |
| V*Source | — | 94.7% | Not comparable |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Frequently Asked Questions (2)
Which is better, LFM2.5-VL-450M or Qwen3.6-27B?
LFM2.5-VL-450M and Qwen3.6-27B 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 Qwen3.6-27B?
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 53.3 versus 20.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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