Model comparison
GLM-5 vs o3-mini
Head-to-head evidence from 8 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #30 (Supported); o3-mini #141 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and o3-mini share 8 comparable benchmark results. 3 of 8 categories are comparable. 41 results are unique to GLM-5; 2 to o3-mini.
Updated July 24, 2026- Shared results
- 8
- GLM-5 only
- 41
- o3-mini only
- 2
- Comparable categories
- 3 / 8
Pick GLM-5 if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 8 shared benchmark results across 4 evidence categories; 3 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 is clearly ahead on the BenchAlign aggregate, 65.29 to 46.59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in coding, where it averages 66.3 against 49.3. The single biggest benchmark swing on the page is SWE-bench Verified, 77.8% to 49.3%. o3-mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. o3-mini 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.
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 | o3-mini |
|---|---|---|---|
| Coding | GLM-566.3 | Margin← 17.0 | o3-mini49.3 |
| Knowledge | GLM-566.4 | Margin→ 10.8 | o3-mini77.2 |
| Inst. Following | GLM-592.6 | Margin→ 1.3 | o3-mini93.9 |
| Agentic | GLM-556.2 | MarginNo overlap | o3-miniNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | o3-miniNot measured |
| Math | GLM-556.3 | MarginNo overlap | o3-miniNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | o3-miniNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 77.8%B 49.3%Winner: GLM-5Δ 28.5SWE-bench Verified: GLM-5 scored 77.8%; o3-mini scored 49.3%. GLM-5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 86%B 77.2%Winner: GLM-5Δ 8.8GPQA: GLM-5 scored 86%; o3-mini scored 77.2%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 92.6%B 93.9%Winner: o3-miniΔ 1.3IFEval: GLM-5 scored 92.6%; o3-mini scored 93.9%. o3-mini wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | o3-mini | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | o3-mini$1.1 input / $4.4 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | o3-mini160 tok/s | o3-mini has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-51.64 s | o3-mini7.12 s | GLM-5 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | o3-mini200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | o3-mini | 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% | 28.7% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
CodingGLM-5 wins7 benchmarks
| Benchmark | GLM-5 | o3-mini | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 49.3% | GLM-5 leads |
| 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% | 39.9% | GLM-5 leads |
Reasoning4 benchmarks
Knowledgeo3-mini wins13 benchmarks
| Benchmark | GLM-5 | o3-mini | Result |
|---|---|---|---|
| GPQASource | 86% | 77.2% | GLM-5 leads |
| 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% | 19.0% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 74.8% | GLM-5 leads |
| AA-HLESource | 27.2% | 8.7% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
| MMLUSource | — | 86.9% | Not comparable |
Math9 benchmarks
| Benchmark | GLM-5 | o3-mini | 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 |
| AIME 2024Source | — | 87.3% | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | o3-mini | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1278 | — | Not comparable |
Frequently Asked Questions (4)
Which is better, GLM-5 or o3-mini?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.29 to 46.59. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 49.3%.
Which is better for knowledge tasks, GLM-5 or o3-mini?
o3-mini has the edge for knowledge tasks in this comparison, averaging 77.2 versus 66.4. Inside this category, Artificial Analysis Intelligence Index is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or o3-mini?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 49.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or o3-mini?
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 92.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.