Model comparison
Gemini 1.5 Pro vs GLM-5
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 1.5 Pro #185 (Supported); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 1.5 Pro and GLM-5 share 4 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to Gemini 1.5 Pro; 45 to GLM-5.
Updated July 20, 2026- Shared results
- 4
- Gemini 1.5 Pro only
- 2
- GLM-5 only
- 45
- Comparable categories
- 0 / 8
Benchmark data for Gemini 1.5 Pro and GLM-5 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 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.
Gemini 1.5 Pro is priced at $1.25 input / $5.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. Gemini 1.5 Pro has the larger context window at 2M, 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 | Gemini 1.5 Pro | Δ | GLM-5 |
|---|---|---|---|
| Agentic | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-556.2 |
| Coding | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-566.3 |
| Reasoning | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-560.8 |
| Knowledge | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-566.4 |
| Math | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-556.3 |
| Multilingual | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | Gemini 1.5 ProNot measured | MarginNo overlap | GLM-592.6 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 1.5 Pro | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 1.5 Pro$1.25 input / $5 output | GLM-5$1 input / $3.2 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | Gemini 1.5 ProNot available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 1.5 ProNot available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 1.5 Pro2M | GLM-5200K | Gemini 1.5 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | Gemini 1.5 Pro | GLM-5 | 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 |
Coding8 benchmarks
| Benchmark | Gemini 1.5 Pro | GLM-5 | Result |
|---|---|---|---|
| AA Coding IndexSource | 23.6% | — | Not comparable |
| AA-SciCodeSource | 29.5% | 46.2% | GLM-5 leads |
| 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 |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | Gemini 1.5 Pro | GLM-5 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 10.0% | 39.5% | GLM-5 leads |
| AA-GPQA DiamondSource | 58.9% | 82.0% | GLM-5 leads |
| AA-HLESource | 4.9% | 27.2% | GLM-5 leads |
| 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 |
| 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 | Gemini 1.5 Pro | GLM-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
Multimodal2 benchmarks
Frequently Asked Questions (3)
Can I compare Gemini 1.5 Pro and GLM-5 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 Gemini 1.5 Pro and GLM-5 today?
Gemini 1.5 Pro: $1.25 input / $5.00 output per 1M tokens 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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