Model comparison
GPT-5.2 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-5.2 #23; LFM2.5-8B-A1B unranked
BenchAlign evidence: GPT-5.2 estimated; LFM2.5-8B-A1B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2 and LFM2.5-8B-A1B share 12 comparable benchmark results. 1 of 8 categories are comparable. 17 results are unique to GPT-5.2; 6 to LFM2.5-8B-A1B.
Updated July 16, 2026- Shared results
- 12
- GPT-5.2 only
- 17
- LFM2.5-8B-A1B only
- 6
- Comparable categories
- 1 / 8
Pick GPT-5.2 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-5.2 is clearly ahead on the provisional aggregate, 74 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 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-5.2 gives you the larger context window at 400K, 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-5.2 | Δ | LFM2.5-8B-A1B |
|---|---|---|---|
| Math | GPT-5.235.2 | Margin→ 14.8 | LFM2.5-8B-A1B50.0 |
| Agentic | GPT-5.255.7 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Coding | GPT-5.270.6 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Reasoning | GPT-5.252.9 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Knowledge | GPT-5.292.4 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Multimodal | GPT-5.280.4 | MarginNo overlap | LFM2.5-8B-A1BNot measured |
| Inst. Following | GPT-5.2Not 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-5.2 | LFM2.5-8B-A1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2$1.75 input / $14 output | LFM2.5-8B-A1B$0 input / $0 output | LFM2.5-8B-A1B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.273 tok/s | LFM2.5-8B-A1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.2130.34 s | LFM2.5-8B-A1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.2400K | LFM2.5-8B-A1B128K | GPT-5.2 lists the larger context window. |
Benchmark Deep Dive
Agentic6 benchmarks
Coding5 benchmarks
Reasoning3 benchmarks
Knowledge7 benchmarks
| Benchmark | GPT-5.2 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| GPQASource | 92.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 42.2% | 8.3% | GPT-5.2 leads |
| AA-GPQA DiamondSource | 90.3% | 51.3% | GPT-5.2 leads |
| AA-HLESource | 35.4% | 6.9% | GPT-5.2 leads |
| AA-Omniscience IndexSource | -1.0% | -33.3% | GPT-5.2 leads |
| AA-Omniscience AccuracySource | 43.8% | 9.4% | GPT-5.2 leads |
| AA-Omniscience Hallucination RateSource | 79.7% | 47.0% | LFM2.5-8B-A1B leads |
MathLFM2.5-8B-A1B wins6 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.2 or LFM2.5-8B-A1B?
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 74 to 37.
Which is better for math, GPT-5.2 or LFM2.5-8B-A1B?
LFM2.5-8B-A1B has the edge for math in this comparison, averaging 50 versus 35.2. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
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