Model comparison
GLM-5 vs MiMo-V2.5
Head-to-head evidence from 5 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GLM-5 #15; MiMo-V2.5 unranked
BenchAlign evidence: GLM-5 supported; MiMo-V2.5 estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and MiMo-V2.5 share 5 comparable benchmark results. 2 of 8 categories are comparable. 45 results are unique to GLM-5; 6 to MiMo-V2.5.
Updated July 15, 2026- Shared results
- 5
- GLM-5 only
- 45
- MiMo-V2.5 only
- 6
- Comparable categories
- 2 / 8
Pick MiMo-V2.5 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 5 shared benchmark results across 3 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
MiMo-V2.5 finishes one point ahead on BenchLM's provisional leaderboard, 64 to 63. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
MiMo-V2.5's sharpest advantage is in agentic, where it averages 65.8 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 65.8%. GLM-5 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5 is the reasoning model in the pair, while GLM-5 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. MiMo-V2.5 gives you the larger context window at 1M, compared with 200K for GLM-5.
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 | MiMo-V2.5 |
|---|---|---|---|
| Coding | GLM-566.3 | Margin← 10.2 | MiMo-V2.556.1 |
| Agentic | GLM-556.2 | Margin→ 9.6 | MiMo-V2.565.8 |
| Reasoning | GLM-560.8 | MarginNo overlap | MiMo-V2.5Not measured |
| Knowledge | GLM-566.6 | MarginNo overlap | MiMo-V2.5Not measured |
| Math | GLM-556.3 | MarginNo overlap | MiMo-V2.5Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | MiMo-V2.5Not measured |
| Multimodal | GLM-5Not measured | MarginNo overlap | MiMo-V2.579.0 |
| Inst. Following | GLM-592.6 | MarginNo overlap | MiMo-V2.5Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 65.8%Winner: MiMo-V2.5Δ 9.6Terminal-Bench 2.0: GLM-5 scored 56.2%; MiMo-V2.5 scored 65.8%. MiMo-V2.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 56.1%Winner: MiMo-V2.5Δ 1SWE-bench Pro: GLM-5 scored 55.1%; MiMo-V2.5 scored 56.1%. MiMo-V2.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | MiMo-V2.5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | MiMo-V2.5Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | MiMo-V2.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | MiMo-V2.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | MiMo-V2.51M | MiMo-V2.5 lists the larger context window. |
Benchmark Deep Dive
AgenticMiMo-V2.5 wins15 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 65.8% | MiMo-V2.5 leads |
| Claw-EvalSource | 57.7% | 62.3% | MiMo-V2.5 leads |
| 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% | 46.89% | GLM-5 leads |
| MM-ClawBenchSource | — | 23.8% | Not comparable |
| ResearchClawBenchSource | — | 16.9% | Not comparable |
CodingGLM-5 wins9 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 56.1% | MiMo-V2.5 leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.2% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 65.8% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | MiMo-V2.5 | 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% | — | 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 | MiMo-V2.5 | 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 MiMo-V2.5?
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 64 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 65.8%.
Which is better for coding, GLM-5 or MiMo-V2.5?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 56.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or MiMo-V2.5?
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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