Model comparison
GLM-5 vs o3-pro
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: GLM-5 #16; o3-pro unranked
Evidence parity. GLM-5 and o3-pro share 2 comparable benchmark results. 0 of 8 categories are comparable. 48 results are unique to GLM-5; 0 to o3-pro.
Updated July 14, 2026- Shared results
- 2
- GLM-5 only
- 48
- o3-pro only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GLM-5 and o3-pro is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 1 evidence category; 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.
o3-pro is priced at $20.00 input / $80.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens 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 | o3-pro |
|---|---|---|---|
| Agentic | GLM-556.2 | MarginNo overlap | o3-proNot measured |
| Coding | GLM-563.3 | MarginNo overlap | o3-proNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | o3-proNot measured |
| Knowledge | GLM-566.6 | MarginNo overlap | o3-proNot measured |
| Math | GLM-556.3 | MarginNo overlap | o3-proNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | o3-proNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | o3-proNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | o3-pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | o3-pro$20 input / $80 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | o3-pro27 tok/s | GLM-5 has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-51.64 s | o3-pro84.93 s | GLM-5 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | o3-pro200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | o3-pro | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | — | Not comparable |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| TAU3-BenchSource | 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 |
| Tau2-TelecomSource | 98.2% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
Coding8 benchmarks
| Benchmark | GLM-5 | o3-pro | 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 |
| Terminal-Bench HardSource | 43.2% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | o3-pro | 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% | 32.5% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 84.5% | o3-pro leads |
| 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 | o3-pro | 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
Multimodal1 benchmarks
| Benchmark | GLM-5 | o3-pro | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1282 | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GLM-5 and o3-pro 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 o3-pro today?
GLM-5: $1.00 input / $3.20 output per 1M tokens o3-pro: $20.00 input / $80.00 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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