Model comparison
GLM-5 vs LFM2.5-VL-1.6B-Extract
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); LFM2.5-VL-1.6B-Extract unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and LFM2.5-VL-1.6B-Extract share 11 comparable benchmark results. 0 of 8 categories are comparable. 38 results are unique to GLM-5; 4 to LFM2.5-VL-1.6B-Extract.
Updated July 23, 2026- Shared results
- 11
- GLM-5 only
- 38
- LFM2.5-VL-1.6B-Extract only
- 4
- Comparable categories
- 0 / 8
Benchmark data for GLM-5 and LFM2.5-VL-1.6B-Extract is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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-5 has the larger context window at 200K, compared with 128K for LFM2.5-VL-1.6B-Extract.
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-5 | Δ | LFM2.5-VL-1.6B-Extract |
|---|---|---|---|
| Agentic | GLM-556.2 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
| Coding | GLM-566.3 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
| Knowledge | GLM-566.4 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
| Math | GLM-556.3 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | LFM2.5-VL-1.6B-Extract | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | LFM2.5-VL-1.6B-ExtractNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | LFM2.5-VL-1.6B-ExtractNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | LFM2.5-VL-1.6B-ExtractNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | LFM2.5-VL-1.6B-Extract128K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | — | Not comparable |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 8.5% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | — | Not comparable |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | 3.0% | GLM-5 leads |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | 1.0% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 28.9% | GLM-5 leads |
| AA-HLESource | 27.2% | 5.1% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -83.9% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 5.2% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 94.0% | GLM-5 leads |
Math8 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (3)
Can I compare GLM-5 and LFM2.5-VL-1.6B-Extract 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-5 and LFM2.5-VL-1.6B-Extract today?
GLM-5: $1.00 input / $3.20 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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