Model comparison
GPT-OSS 120B vs LFM2.5-VL-1.6B-Extract
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-OSS 120B #116 (Supported); LFM2.5-VL-1.6B-Extract unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-OSS 120B and LFM2.5-VL-1.6B-Extract share 11 comparable benchmark results. 0 of 8 categories are comparable. 15 results are unique to GPT-OSS 120B; 4 to LFM2.5-VL-1.6B-Extract.
Updated July 23, 2026- Shared results
- 11
- GPT-OSS 120B only
- 15
- LFM2.5-VL-1.6B-Extract only
- 4
- Comparable categories
- 0 / 8
Benchmark data for GPT-OSS 120B and LFM2.5-VL-1.6B-Extract is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-OSS 120B | LFM2.5-VL-1.6B-Extract | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-OSS 120B$0 input / $0 output | LFM2.5-VL-1.6B-ExtractNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-OSS 120B262 tok/s | LFM2.5-VL-1.6B-ExtractNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-OSS 120B0.79 s | LFM2.5-VL-1.6B-ExtractNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-OSS 120B128K | LFM2.5-VL-1.6B-Extract128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GPT-OSS 120B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA Agentic IndexSource | 13.2% | — | Not comparable |
| APEX-Agents-AASource | 3.1% | — | Not comparable |
| τ²-bench resultsSource | 65.8% | 8.5% | GPT-OSS 120B leads |
| GDPval-AASource | 15.0% | — | Not comparable |
| GDPval-AASource | 799 | — | Not comparable |
| Gert LabsSource | 29.61% | — | Not comparable |
| AA EnterpriseOps-GymSource | 25.5% | — | Not comparable |
| AA ITBenchSource | 5.6% | — | Not comparable |
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-OSS 120B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 23.8% | 1.0% | GPT-OSS 120B leads |
| AA-GPQA DiamondSource | 78.2% | 28.9% | GPT-OSS 120B leads |
| AA-HLESource | 18.5% | 5.1% | GPT-OSS 120B leads |
| AA-Omniscience IndexSource | -50.0% | -83.9% | GPT-OSS 120B leads |
| AA-Omniscience AccuracySource | 21.5% | 5.2% | GPT-OSS 120B leads |
| AA-Omniscience Hallucination RateSource | 91.2% | 94.0% | GPT-OSS 120B leads |
| AA Openness IndexSource | 38.9% | — | Not comparable |
| AA MMLU-ProSource | 80.8% | — | Not comparable |
Math1 benchmarks
| Benchmark | GPT-OSS 120B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA AIME 2025Source | 93.4% | — | Not comparable |
Multilingual1 benchmarks
| Benchmark | GPT-OSS 120B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 82.8% | — | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-OSS 120B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA-IFBenchSource | 69.0% | 33.1% | GPT-OSS 120B leads |
Frequently Asked Questions (3)
Can I compare GPT-OSS 120B and LFM2.5-VL-1.6B-Extract 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 GPT-OSS 120B and LFM2.5-VL-1.6B-Extract today?
GPT-OSS 120B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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