Model comparison
GPT-5.6 Sol vs LFM2.5-230M
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.6 Sol #3 (Supported); LFM2.5-230M unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and LFM2.5-230M share 2 comparable benchmark results. 1 of 8 categories are comparable. 44 results are unique to GPT-5.6 Sol; 4 to LFM2.5-230M.
Updated July 23, 2026- Shared results
- 2
- GPT-5.6 Sol only
- 44
- LFM2.5-230M only
- 4
- Comparable categories
- 1 / 8
Treat this as a split decision. GPT-5.6 Sol makes more sense if knowledge is the priority or you need the larger 1M context window; LFM2.5-230M is the better fit if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 1 evidence category; 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 and LFM2.5-230M 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.
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 LFM2.5-230M. That is roughly Infinityx on output cost alone. GPT-5.6 Sol is the reasoning model in the pair, while LFM2.5-230M 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. GPT-5.6 Sol gives you the larger context window at 1M, compared with 32K for LFM2.5-230M.
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 | LFM2.5-230M |
|---|---|---|---|
| Knowledge | GPT-5.6 Sol94.6 | Margin← 73.4 | LFM2.5-230M21.2 |
| Agentic | GPT-5.6 Sol92.0 | MarginNo overlap | LFM2.5-230MNot measured |
| Coding | GPT-5.6 Sol64.6 | MarginNo overlap | LFM2.5-230MNot measured |
| Math | GPT-5.6 Sol87.5 | MarginNo overlap | LFM2.5-230MNot measured |
| Multimodal | GPT-5.6 Sol83.0 | MarginNo overlap | LFM2.5-230MNot measured |
| Inst. Following | GPT-5.6 SolNot measured | MarginNo overlap | LFM2.5-230M50.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 94.6%B 25.4%Winner: GPT-5.6 SolΔ 69.2GPQA: GPT-5.6 Sol scored 94.6%; LFM2.5-230M scored 25.4%. GPT-5.6 Sol wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.6 Sol | LFM2.5-230M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | LFM2.5-230M$0 input / $0 output | LFM2.5-230M has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | LFM2.5-230MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | LFM2.5-230MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | LFM2.5-230M32K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | GPT-5.6 Sol | LFM2.5-230M | 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% | — | Not comparable |
| 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 | — | 21.0% | Not comparable |
Coding8 benchmarks
| Benchmark | GPT-5.6 Sol | LFM2.5-230M | 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% | — | Not comparable |
Reasoning4 benchmarks
KnowledgeGPT-5.6 Sol wins11 benchmarks
| Benchmark | GPT-5.6 Sol | LFM2.5-230M | Result |
|---|---|---|---|
| GPQASource | 94.6% | 25.4% | GPT-5.6 Sol leads |
| GPQA-DSource | 94.6% | 25.4% | GPT-5.6 Sol leads |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | — | Not comparable |
| AA-GPQA DiamondSource | 94.1% | — | Not comparable |
| AA-HLESource | 47.2% | — | Not comparable |
| AA-Omniscience IndexSource | 21.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 58.5% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 88.8% | — | Not comparable |
| MMLU-ProSource | — | 20.3% | Not comparable |
Math3 benchmarks
Multimodal3 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.6 Sol or LFM2.5-230M?
GPT-5.6 Sol and LFM2.5-230M 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, GPT-5.6 Sol or LFM2.5-230M?
GPT-5.6 Sol has the edge for knowledge tasks in this comparison, averaging 94.6 versus 21.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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