Model comparison
Claude Opus 4.7 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.
BenchAlign evidence: Claude Opus 4.7 supported; LFM2.5-8B-A1B estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 and LFM2.5-8B-A1B share 12 comparable benchmark results. 1 of 8 categories are comparable. 10 results are unique to Claude Opus 4.7; 6 to LFM2.5-8B-A1B.
Updated July 16, 2026- Shared results
- 12
- Claude Opus 4.7 only
- 10
- LFM2.5-8B-A1B only
- 6
- Comparable categories
- 1 / 8
Pick Claude Opus 4.7 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
Claude Opus 4.7 is clearly ahead on the provisional aggregate, 69 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Opus 4.7 is also the more expensive model on tokens at $5.00 input / $25.00 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. LFM2.5-8B-A1B is the reasoning model in the pair, while Claude Opus 4.7 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. Claude Opus 4.7 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 | Claude Opus 4.7 | Δ | LFM2.5-8B-A1B |
|---|---|---|---|
| Math | Claude Opus 4.738.6 | Margin→ 11.4 | LFM2.5-8B-A1B50.0 |
| Inst. Following | Claude Opus 4.7Not 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 | Claude Opus 4.7 | LFM2.5-8B-A1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7$5 input / $25 output | LFM2.5-8B-A1B$0 input / $0 output | LFM2.5-8B-A1B has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.7Not available | LFM2.5-8B-A1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7Not available | LFM2.5-8B-A1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.71M | LFM2.5-8B-A1B128K | Claude Opus 4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding5 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Claude Opus 4.7 | LFM2.5-8B-A1B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 42.7% | 8.3% | Claude Opus 4.7 leads |
| AA-GPQA DiamondSource | 88.5% | 51.3% | Claude Opus 4.7 leads |
| AA-HLESource | 31.2% | 6.9% | Claude Opus 4.7 leads |
| AA-Omniscience IndexSource | 14.2% | -33.3% | Claude Opus 4.7 leads |
| AA-Omniscience AccuracySource | 43.5% | 9.4% | Claude Opus 4.7 leads |
| AA-Omniscience Hallucination RateSource | 51.9% | 47.0% | LFM2.5-8B-A1B leads |
MathLFM2.5-8B-A1B wins5 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, Claude Opus 4.7 or LFM2.5-8B-A1B?
Claude Opus 4.7 is ahead on BenchLM's provisional leaderboard, 69 to 37.
Which is better for math, Claude Opus 4.7 or LFM2.5-8B-A1B?
LFM2.5-8B-A1B has the edge for math in this comparison, averaging 50 versus 38.6. Claude Opus 4.7 stays close enough that the answer can still flip depending on your workload.
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