Model comparison
GLM-5 vs LFM2.5-VL-450M
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); LFM2.5-VL-450M 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-450M share 3 comparable benchmark results. 2 of 8 categories are comparable. 46 results are unique to GLM-5; 4 to LFM2.5-VL-450M.
Updated July 18, 2026- Shared results
- 3
- GLM-5 only
- 46
- LFM2.5-VL-450M only
- 4
- Comparable categories
- 2 / 8
Treat this as a split decision. GLM-5 makes more sense if knowledge is the priority or you need the larger 200K context window; LFM2.5-VL-450M is the better fit if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5 and LFM2.5-VL-450M finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-VL-450M. That is roughly Infinityx on output cost alone. GLM-5 gives you the larger context window at 200K, compared with 128K for LFM2.5-VL-450M.
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-450M |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin← 45.9 | LFM2.5-VL-450M20.5 |
| Inst. Following | GLM-592.6 | Margin← 31.4 | LFM2.5-VL-450M61.2 |
| Agentic | GLM-556.2 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Coding | GLM-566.3 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Math | GLM-556.3 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | LFM2.5-VL-450MNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 85.7%B 19.3%Winner: GLM-5Δ 66.4MMLU-Pro: GLM-5 scored 85.7%; LFM2.5-VL-450M scored 19.3%. GLM-5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 86%B 25.7%Winner: GLM-5Δ 60.3GPQA: GLM-5 scored 86%; LFM2.5-VL-450M scored 25.7%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 92.6%B 61.2%Winner: GLM-5Δ 31.4IFEval: GLM-5 scored 92.6%; LFM2.5-VL-450M scored 61.2%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | LFM2.5-VL-450M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | LFM2.5-VL-450M$0 input / $0 output | LFM2.5-VL-450M has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | LFM2.5-VL-450MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | LFM2.5-VL-450MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | LFM2.5-VL-450M128K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-450M | 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% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| BFCL v4Source | — | 21.1% | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-450M | 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% | — | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5 wins12 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| GPQASource | 86% | 25.7% | GLM-5 leads |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | 19.3% | GLM-5 leads |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 82.0% | — | Not comparable |
| AA-HLESource | 27.2% | — | Not comparable |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | LFM2.5-VL-450M | 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
Multimodal4 benchmarks
Frequently Asked Questions (3)
Which is better, GLM-5 or LFM2.5-VL-450M?
GLM-5 and LFM2.5-VL-450M are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, GLM-5 or LFM2.5-VL-450M?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 20.5. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or LFM2.5-VL-450M?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 61.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.