Model comparison
DeepSeek V4 Pro (Max) vs GLM-5
Head-to-head evidence from 24 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro (Max) unranked (Not scored); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro (Max) and GLM-5 share 24 comparable benchmark results. 4 of 8 categories are comparable. 24 results are unique to DeepSeek V4 Pro (Max); 25 to GLM-5.
Updated July 23, 2026- Shared results
- 24
- DeepSeek V4 Pro (Max) only
- 24
- GLM-5 only
- 25
- Comparable categories
- 4 / 8
Treat this as a split decision. DeepSeek V4 Pro (Max) makes more sense if mathematics is the priority or you want the cheaper token bill; GLM-5 is the better fit if knowledge 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 24 shared benchmark results across 7 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V4 Pro (Max) and GLM-5 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 $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro (Max). That is roughly 3.7x on output cost alone. DeepSeek V4 Pro (Max) 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. DeepSeek V4 Pro (Max) 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 | DeepSeek V4 Pro (Max) | Δ | GLM-5 |
|---|---|---|---|
| Math | DeepSeek V4 Pro (Max)95.2 | Margin← 38.9 | GLM-556.3 |
| Agentic | DeepSeek V4 Pro (Max)74.5 | Margin← 18.3 | GLM-556.2 |
| Knowledge | DeepSeek V4 Pro (Max)60.1 | Margin→ 6.3 | GLM-566.4 |
| Coding | DeepSeek V4 Pro (Max)70.9 | Margin← 4.6 | GLM-566.3 |
| Reasoning | DeepSeek V4 Pro (Max)Not measured | MarginNo overlap | GLM-560.8 |
| Multilingual | DeepSeek V4 Pro (Max)Not measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | DeepSeek V4 Pro (Max)Not measured | MarginNo overlap | GLM-592.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 37.7%B 50.4%Winner: GLM-5Δ 12.7HLE: DeepSeek V4 Pro (Max) scored 37.7%; GLM-5 scored 50.4%. GLM-5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 67.9%B 56.2%Winner: DeepSeek V4 Pro (Max)Δ 11.7Terminal-Bench 2.0: DeepSeek V4 Pro (Max) scored 67.9%; GLM-5 scored 56.2%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 95.2%B 86.4%Winner: DeepSeek V4 Pro (Max)Δ 8.8HMMT Feb 2026: DeepSeek V4 Pro (Max) scored 95.2%; GLM-5 scored 86.4%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
GPQA
KnowledgeA 90.1%B 86%Winner: DeepSeek V4 Pro (Max)Δ 4.1GPQA: DeepSeek V4 Pro (Max) scored 90.1%; GLM-5 scored 86%. DeepSeek V4 Pro (Max) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 80.6%B 77.8%Winner: DeepSeek V4 Pro (Max)Δ 2.8SWE-bench Verified: DeepSeek V4 Pro (Max) scored 80.6%; GLM-5 scored 77.8%. DeepSeek V4 Pro (Max) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro (Max) | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | GLM-5$1 input / $3.2 output | DeepSeek V4 Pro (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Pro (Max)Not available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro (Max)Not available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro (Max)1M | GLM-5200K | DeepSeek V4 Pro (Max) lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Pro (Max) wins25 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 67.9% | 56.2% | DeepSeek V4 Pro (Max) leads |
| BrowseCompSource | 83.4% | — | Not comparable |
| HLE w/ toolsSource | 48.2% | — | Not comparable |
| MCP AtlasSource | 73.6% | 31.1% | DeepSeek V4 Pro (Max) leads |
| GDPval-AASource | 1307 | — | Not comparable |
| ToolathlonSource | 51.8% | 38% | DeepSeek V4 Pro (Max) leads |
| AA Agentic IndexSource | 36.4% | — | Not comparable |
| APEX-Agents-AASource | 24.3% | 14.5% | DeepSeek V4 Pro (Max) leads |
| τ²-bench resultsSource | 96.2% | 98.2% | GLM-5 leads |
| GDPval-AASource | 40.4% | — | Not comparable |
| AA BriefcaseSource | 932 | — | Not comparable |
| AA EnterpriseOps-GymSource | 40.4% | — | Not comparable |
| AA Harvey LABSource | 84.4% | — | Not comparable |
| AA ITBenchSource | 38.3% | — | Not comparable |
| AA Tau3 BankingSource | 25.8% | — | Not comparable |
| terminalBenchHardSource | 46.2% | — | Not comparable |
| aaTerminalBench21Source | 64% | — | 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 |
| MCP-TasksSource | — | 60.8% | Not comparable |
| WideResearchSource | — | 69.8% | Not comparable |
| CyberGymSource | — | 43.2% | Not comparable |
| Gert LabsSource | — | 50.99% | Not comparable |
CodingDeepSeek V4 Pro (Max) wins11 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GLM-5 | Result |
|---|---|---|---|
| CodeforcesSource | 3206.0 | — | Not comparable |
| SWE-bench VerifiedSource | 80.6% | 77.8% | DeepSeek V4 Pro (Max) leads |
| SWE-bench ProSource | 55.4% | 55.1% | DeepSeek V4 Pro (Max) leads |
| SWE MultilingualSource | 76.2% | 73.3% | DeepSeek V4 Pro (Max) leads |
| Terminal-Bench 2.0Source | 67.9% | — | Not comparable |
| Vibe Code BenchSource | 49.93% | — | Not comparable |
| AA Coding IndexSource | 59.4% | — | Not comparable |
| AA-SciCodeSource | 50.0% | 46.2% | DeepSeek V4 Pro (Max) leads |
| SWE-bench Verified*Source | — | 72.8% | Not comparable |
| SWE-RebenchSource | — | 62.8% | Not comparable |
| React Native EvalsSource | — | 74.8% | Not comparable |
Reasoning6 benchmarks
KnowledgeGLM-5 wins15 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GLM-5 | Result |
|---|---|---|---|
| MMLU-ProSource | 87.5% | 85.7% | DeepSeek V4 Pro (Max) leads |
| SimpleQASource | 57.9% | — | Not comparable |
| Chinese-SimpleQASource | 84.4% | — | Not comparable |
| GPQASource | 90.1% | 86% | DeepSeek V4 Pro (Max) leads |
| GPQA-DSource | 90.1% | 86.0% | DeepSeek V4 Pro (Max) leads |
| HLESource | 37.7% | 50.4% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 44.3% | 39.5% | DeepSeek V4 Pro (Max) leads |
| AA-GPQA DiamondSource | 88.8% | 82.0% | DeepSeek V4 Pro (Max) leads |
| AA-HLESource | 35.9% | 27.2% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience IndexSource | -10.0% | 2.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 43.3% | 26.9% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 34.0% | GLM-5 leads |
| AA Openness IndexSource | 50.0% | — | Not comparable |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
MathDeepSeek V4 Pro (Max) wins11 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | GLM-5 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 95.2% | 86.4% | DeepSeek V4 Pro (Max) leads |
| IMOAnswerBenchSource | 89.8% | — | Not comparable |
| ApexSource | 38.3% | — | Not comparable |
| Apex ShortlistSource | 90.2% | — | Not comparable |
| 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 |
| 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 | DeepSeek V4 Pro (Max) | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1264 | 1278 | GLM-5 leads |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Pro (Max) or GLM-5?
DeepSeek V4 Pro (Max) and GLM-5 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, DeepSeek V4 Pro (Max) or GLM-5?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 60.1. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Pro (Max) or GLM-5?
DeepSeek V4 Pro (Max) has the edge for coding in this comparison, averaging 70.9 versus 66.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V4 Pro (Max) or GLM-5?
DeepSeek V4 Pro (Max) has the edge for math in this comparison, averaging 95.2 versus 56.3. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Pro (Max) or GLM-5?
DeepSeek V4 Pro (Max) has the edge for agentic tasks in this comparison, averaging 74.5 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
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