Model comparison
GLM-4.7 vs LFM2.5-ColBERT-350M
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: GLM-4.7 #32; LFM2.5-ColBERT-350M unranked
Evidence parity. GLM-4.7 and LFM2.5-ColBERT-350M share 0 comparable benchmark results. 0 of 8 categories are comparable. 31 results are unique to GLM-4.7; 2 to LFM2.5-ColBERT-350M.
Updated July 14, 2026- Shared results
- 0
- GLM-4.7 only
- 31
- LFM2.5-ColBERT-350M only
- 2
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.7 and LFM2.5-ColBERT-350M is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GLM-4.7 has the larger context window at 200K, compared with 32K for LFM2.5-ColBERT-350M.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | LFM2.5-ColBERT-350M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | LFM2.5-ColBERT-350M$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | LFM2.5-ColBERT-350MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | LFM2.5-ColBERT-350MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | LFM2.5-ColBERT-350M32K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-ColBERT-350M | 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 |
Coding7 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-ColBERT-350M | 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
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-ColBERT-350M | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| 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 |
Math3 benchmarks
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-ColBERT-350M | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | LFM2.5-ColBERT-350M | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GLM-4.7 and LFM2.5-ColBERT-350M on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GLM-4.7 and LFM2.5-ColBERT-350M today?
GLM-4.7: $0.00 input / $0.00 output per 1M tokens LFM2.5-ColBERT-350M: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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