Model comparison
GPT-5.5 vs LFM2.5-8B-A1B
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-5.5 #3; LFM2.5-8B-A1B unranked
BenchAlign evidence: GPT-5.5 supported; LFM2.5-8B-A1B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and LFM2.5-8B-A1B share 12 comparable benchmark results. 1 of 8 categories are comparable. 45 results are unique to GPT-5.5; 6 to LFM2.5-8B-A1B.
Updated July 16, 2026- Shared results
- 12
- GPT-5.5 only
- 45
- LFM2.5-8B-A1B only
- 6
- Comparable categories
- 1 / 8
Pick GPT-5.5 if you want the stronger benchmark profile. LFM2.5-8B-A1B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 12 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-5.5 is clearly ahead on the provisional aggregate, 78 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5 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-5.5 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-5.5 | Δ | LFM2.5-8B-A1B |
|---|---|---|---|
| Math | GPT-5.547.6 | Margin→ 2.4 | LFM2.5-8B-A1B50.0 |
| Agentic | GPT-5.581.6 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Coding | GPT-5.558.6 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Reasoning | GPT-5.585.0 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Knowledge | GPT-5.557.8 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Multimodal | GPT-5.570.4 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Inst. Following | GPT-5.5Not 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-5.5 | LFM2.5-8B-A1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$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-5.5Not available | LFM2.5-8B-A1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | LFM2.5-8B-A1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | LFM2.5-8B-A1B128K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
Agentic23 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | — | Not comparable |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | — | Not comparable |
| OSWorld-VerifiedSource | 78.7% | — | Not comparable |
| MCP AtlasSource | 75.3% | — | Not comparable |
| ToolathlonSource | 55.6% | — | Not comparable |
| τ²-bench resultsSource | 93.9% | 16.1% | GPT-5.5 leads |
| AA Agentic IndexSource | 44.9% | — | Not comparable |
| APEX-Agents-AASource | 37.7% | — | Not comparable |
| GDPval-AASource | 49.7% | — | Not comparable |
| GDPval-AASource | 1493 | — | Not comparable |
| Gert LabsSource | 72.93% | — | Not comparable |
| ResearchClawBenchSource | 17.0% | — | Not comparable |
| OSWorld 2.0Source | 13.0% | — | Not comparable |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA BriefcaseSource | 1154 | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | — | Not comparable |
| AA Harvey LABSource | 4.2% | — | Not comparable |
| AA ITBenchSource | 45.8% | — | Not comparable |
| AA Tau3 BankingSource | 31.3% | — | Not comparable |
| BFCL v4Source | — | 49.7% | Not comparable |
Coding11 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 82.0% | — | Not comparable |
| Vibe Code BenchSource | 69.85% | — | Not comparable |
| React Native EvalsSource | 84.7% | — | Not comparable |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | — | Not comparable |
| Terminal-Bench HardSource | 60.6% | 4.5% | GPT-5.5 leads |
| AA-SciCodeSource | 56.1% | 7.8% | GPT-5.5 leads |
| FrontierCodeSource | 43.0% | — | Not comparable |
| AA Terminal-Bench 2.1Source | 84.3% | — | Not comparable |
Reasoning5 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.5 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| GPQASource | 93.6% | — | Not comparable |
| GPQA-DSource | 93.6% | — | Not comparable |
| HLESource | 52.2% | — | Not comparable |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | 8.3% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 93.5% | 51.3% | GPT-5.5 leads |
| AA-HLESource | 44.3% | 6.9% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 20.1% | -33.3% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 56.9% | 9.4% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 47.0% | LFM2.5-8B-A1B leads |
MathLFM2.5-8B-A1B wins6 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.5 or LFM2.5-8B-A1B?
GPT-5.5 is ahead on BenchLM's provisional leaderboard, 78 to 37.
Which is better for math, GPT-5.5 or LFM2.5-8B-A1B?
LFM2.5-8B-A1B has the edge for math in this comparison, averaging 50 versus 47.6. GPT-5.5 stays close enough that the answer can still flip depending on your workload.
Related Comparisons
Explore More
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.