Model comparison
GPT-5.6 Sol vs LFM2.5-8B-A1B
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-5.6 Sol #3 (Supported); LFM2.5-8B-A1B #166 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and LFM2.5-8B-A1B share 11 comparable benchmark results. 1 of 8 categories are comparable. 35 results are unique to GPT-5.6 Sol; 6 to LFM2.5-8B-A1B.
Updated July 23, 2026- Shared results
- 11
- GPT-5.6 Sol only
- 35
- LFM2.5-8B-A1B only
- 6
- Comparable categories
- 1 / 8
Pick GPT-5.6 Sol if you want the stronger benchmark profile. LFM2.5-8B-A1B only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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
GPT-5.6 Sol is clearly ahead on the BenchAlign aggregate, 81.96 to 41.42. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.6 Sol's sharpest advantage is in mathematics, where it averages 87.5 against 50.
GPT-5.6 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-8B-A1B. That is roughly Infinityx on output cost alone. GPT-5.6 Sol gives you the larger context window at 1M, compared with 128K for LFM2.5-8B-A1B.
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 | GPT-5.6 Sol | Δ | LFM2.5-8B-A1B |
|---|---|---|---|
| Math | GPT-5.6 Sol87.5 | Margin← 37.5 | LFM2.5-8B-A1B50.0 |
| Agentic | GPT-5.6 Sol92.0 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Coding | GPT-5.6 Sol64.6 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Knowledge | GPT-5.6 Sol94.6 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Multimodal | GPT-5.6 Sol83.0 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Inst. Following | GPT-5.6 SolNot measured | MarginNo overlap | LFM2.5-8B-A1B68.8 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.6 Sol | LFM2.5-8B-A1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | LFM2.5-8B-A1B$0 input / $0 output | LFM2.5-8B-A1B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | LFM2.5-8B-A1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | LFM2.5-8B-A1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | LFM2.5-8B-A1B128K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | GPT-5.6 Sol | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| BrowseCompSource | 92.2% | — | Not comparable |
| OSWorld 2.0Source | 62.6% | — | Not comparable |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | — | Not comparable |
| AA Agentic IndexSource | 54.0% | — | Not comparable |
| τ²-bench resultsSource | 85.1% | 16.1% | GPT-5.6 Sol leads |
| GDPval-AASource | 61.8% | — | Not comparable |
| GDPval-AASource | 1736 | — | Not comparable |
| AA BriefcaseSource | 1501 | — | Not comparable |
| AA ITBenchSource | 56.2% | — | Not comparable |
| AA Tau3 BankingSource | 33.0% | — | Not comparable |
| AA AutomationBenchSource | 51.2% | — | Not comparable |
| AA Harvey LABSource | 87.2% | — | Not comparable |
| terminalBenchHardSource | 65.9% | — | Not comparable |
| aaTerminalBench21Source | 88% | — | Not comparable |
| BFCL v4Source | — | 49.7% | Not comparable |
Coding8 benchmarks
| Benchmark | GPT-5.6 Sol | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| deepSweSource | 72.7% | — | Not comparable |
| FrontierCode 1.1 ExtendedSource | 60.6% | — | Not comparable |
| cursorBench32Source | 67.2% | — | Not comparable |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 77.4% | — | Not comparable |
| AA-SciCodeSource | 56.1% | 7.8% | GPT-5.6 Sol leads |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.6 Sol | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| GPQASource | 94.6% | — | Not comparable |
| GPQA-DSource | 94.6% | — | Not comparable |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 8.3% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 51.3% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 6.9% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | -33.3% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 9.4% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 47.0% | LFM2.5-8B-A1B leads |
MathGPT-5.6 Sol wins6 benchmarks
Multimodal3 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.6 Sol or LFM2.5-8B-A1B?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 41.42.
Which is better for math, GPT-5.6 Sol or LFM2.5-8B-A1B?
GPT-5.6 Sol has the edge for math in this comparison, averaging 87.5 versus 50. LFM2.5-8B-A1B stays close enough that the answer can still flip depending on your workload.
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