Model comparison
GLM-4.7 vs LFM2.5-230M
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: GLM-4.7 #32; LFM2.5-230M unranked
Evidence parity. GLM-4.7 and LFM2.5-230M share 2 comparable benchmark results. 1 of 8 categories are comparable. 29 results are unique to GLM-4.7; 4 to LFM2.5-230M.
Updated July 14, 2026- Shared results
- 2
- GLM-4.7 only
- 29
- LFM2.5-230M only
- 4
- Comparable categories
- 1 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. LFM2.5-230M only becomes the better choice if 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
GLM-4.7 is clearly ahead on the provisional aggregate, 62 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in knowledge, where it averages 52.1 against 21.2. The single biggest benchmark swing on the page is MMLU-Pro, 84.3% to 20.3%.
GLM-4.7 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. GLM-4.7 gives you the larger context window at 200K, 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 | GLM-4.7 | Δ | LFM2.5-230M |
|---|---|---|---|
| Knowledge | GLM-4.752.1 | Margin← 30.9 | LFM2.5-230M21.2 |
| Agentic | GLM-4.745.7 | MarginNo overlap | LFM2.5-230MNot measured |
| Coding | GLM-4.773.8 | MarginNo overlap | LFM2.5-230MNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | LFM2.5-230MNot measured |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | LFM2.5-230M50.1 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | LFM2.5-230M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | LFM2.5-230M$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | LFM2.5-230MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | LFM2.5-230MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | LFM2.5-230M32K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-230M | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| Tau2-TelecomSource | 95.9% | — | Not comparable |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
| BFCL v4Source | — | 21.0% | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-230M | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | — | Not comparable |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | — | Not comparable |
| Terminal-Bench HardSource | 31.8% | — | Not comparable |
| AA-SciCodeSource | 45.1% | — | Not comparable |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
Reasoning2 benchmarks
KnowledgeGLM-4.7 wins10 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-230M | Result |
|---|---|---|---|
| GPQASource | 85.7% | 25.4% | GLM-4.7 leads |
| MMLU-ProSource | 84.3% | 20.3% | GLM-4.7 leads |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | — | Not comparable |
| AA-GPQA DiamondSource | 85.9% | — | Not comparable |
| AA-HLESource | 25.1% | — | Not comparable |
| AA-Omniscience IndexSource | -34.6% | — | Not comparable |
| AA-Omniscience AccuracySource | 29.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 90.3% | — | Not comparable |
| GPQA-DSource | — | 25.4% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-230M | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-4.7 or LFM2.5-230M?
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 62 to 31. The biggest single separator in this matchup is MMLU-Pro, where the scores are 84.3% and 20.3%.
Which is better for knowledge tasks, GLM-4.7 or LFM2.5-230M?
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 52.1 versus 21.2. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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