Model comparison
DeepSeek V4 Flash (Max) vs GLM-5.2
Head-to-head evidence from 32 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Flash (Max) unranked (Not scored); GLM-5.2 #37 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (Max) and GLM-5.2 share 32 comparable benchmark results. 4 of 8 categories are comparable. 13 results are unique to DeepSeek V4 Flash (Max); 11 to GLM-5.2.
Updated July 23, 2026- Shared results
- 32
- DeepSeek V4 Flash (Max) only
- 13
- GLM-5.2 only
- 11
- Comparable categories
- 4 / 8
Treat this as a split decision. DeepSeek V4 Flash (Max) makes more sense if coding is the priority or you want the cheaper token bill; GLM-5.2 is the better fit if agentic is the priority.
Confidence note. This is a partial-evidence comparison with 32 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 Flash (Max) and GLM-5.2 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-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (Max). That is roughly 15.7x on output cost alone.
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 Flash (Max) | Δ | GLM-5.2 |
|---|---|---|---|
| Agentic | DeepSeek V4 Flash (Max)63.8 | Margin→ 17.2 | GLM-5.281.0 |
| Coding | DeepSeek V4 Flash (Max)68.8 | Margin← 6.7 | GLM-5.262.1 |
| Knowledge | DeepSeek V4 Flash (Max)55.3 | Margin→ 4.3 | GLM-5.259.6 |
| Math | DeepSeek V4 Flash (Max)94.8 | Margin→ 1.1 | GLM-5.295.9 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.9%B 81%Winner: GLM-5.2Δ 24.1Terminal-Bench 2.0: DeepSeek V4 Flash (Max) scored 56.9%; GLM-5.2 scored 81%. GLM-5.2 wins this benchmark. - Source ↗
HLE
KnowledgeA 34.8%B 54.7%Winner: GLM-5.2Δ 19.9HLE: DeepSeek V4 Flash (Max) scored 34.8%; GLM-5.2 scored 54.7%. GLM-5.2 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.6%B 62.1%Winner: GLM-5.2Δ 9.5SWE-bench Pro: DeepSeek V4 Flash (Max) scored 52.6%; GLM-5.2 scored 62.1%. GLM-5.2 wins this benchmark. - Source ↗
GPQA
KnowledgeA 88.1%B 91.2%Winner: GLM-5.2Δ 3.1GPQA: DeepSeek V4 Flash (Max) scored 88.1%; GLM-5.2 scored 91.2%. GLM-5.2 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 94.8%B 92.5%Winner: DeepSeek V4 Flash (Max)Δ 2.3HMMT Feb 2026: DeepSeek V4 Flash (Max) scored 94.8%; GLM-5.2 scored 92.5%. DeepSeek V4 Flash (Max) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Flash (Max) | GLM-5.2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (Max)$0.14 input / $0.28 output | GLM-5.2$1.4 input / $4.4 output | DeepSeek V4 Flash (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (Max)Not available | GLM-5.2Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (Max)Not available | GLM-5.2Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (Max)1M | GLM-5.21M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGLM-5.2 wins19 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5.2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.9% | 81% | GLM-5.2 leads |
| BrowseCompSource | 73.2% | — | Not comparable |
| HLE w/ toolsSource | 45.1% | — | Not comparable |
| MCP AtlasSource | 69% | 76.8% | GLM-5.2 leads |
| GDPval-AASource | 1189 | 1514 | GLM-5.2 leads |
| ToolathlonSource | 47.8% | 48.2% | GLM-5.2 leads |
| AA Agentic IndexSource | 31.1% | 43.1% | GLM-5.2 leads |
| τ²-bench resultsSource | 95% | 99.1% | GLM-5.2 leads |
| GDPval-AASource | 34.4% | 50.7% | GLM-5.2 leads |
| AA BriefcaseSource | 831 | 1260 | GLM-5.2 leads |
| AA EnterpriseOps-GymSource | 39.6% | 42.7% | GLM-5.2 leads |
| AA Harvey LABSource | 81.3% | 91.0% | GLM-5.2 leads |
| AA ITBenchSource | 31.5% | 42.7% | GLM-5.2 leads |
| AA Tau3 BankingSource | 22.9% | 26.8% | GLM-5.2 leads |
| terminalBenchHardSource | 35.6% | 50.8% | GLM-5.2 leads |
| APEX-Agents-AASource | — | 33.7% | Not comparable |
| ResearchClawBenchSource | — | 20.7% | Not comparable |
| AA AutomationBenchSource | — | 27.8% | Not comparable |
| aaTerminalBench21Source | — | 77.9% | Not comparable |
CodingDeepSeek V4 Flash (Max) wins10 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5.2 | Result |
|---|---|---|---|
| CodeforcesSource | 3052.0 | — | Not comparable |
| SWE-bench VerifiedSource | 79% | — | Not comparable |
| SWE-bench ProSource | 52.6% | 62.1% | GLM-5.2 leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| Terminal-Bench 2.0Source | 56.9% | 81.0% | GLM-5.2 leads |
| AA Coding IndexSource | 56.2% | 68.8% | GLM-5.2 leads |
| AA-SciCodeSource | 44.9% | 50.5% | GLM-5.2 leads |
| NL2RepoSource | — | 48.9% | Not comparable |
| ProgramBenchSource | — | 63.7% | Not comparable |
| cursorBench32Source | — | 55.0% | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5.2 wins14 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5.2 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.2% | — | Not comparable |
| SimpleQASource | 34.1% | — | Not comparable |
| Chinese-SimpleQASource | 78.9% | — | Not comparable |
| GPQASource | 88.1% | 91.2% | GLM-5.2 leads |
| GPQA-DSource | 88.1% | 91.2% | GLM-5.2 leads |
| HLESource | 34.8% | 54.7% | GLM-5.2 leads |
| Artificial Analysis Intelligence IndexSource | 40.3% | 51.1% | GLM-5.2 leads |
| AA-GPQA DiamondSource | 89.4% | 89.5% | GLM-5.2 leads |
| AA-HLESource | 32.1% | 40.1% | GLM-5.2 leads |
| AA-Omniscience IndexSource | -22.9% | 4.0% | GLM-5.2 leads |
| AA-Omniscience AccuracySource | 37.2% | 25.1% | DeepSeek V4 Flash (Max) leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 28.1% | GLM-5.2 leads |
| AA Openness IndexSource | 50.0% | 44.4% | DeepSeek V4 Flash (Max) leads |
| HLE w/o toolsSource | — | 40.5% | Not comparable |
MathGLM-5.2 wins7 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5.2 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 94.8% | 92.5% | DeepSeek V4 Flash (Max) leads |
| IMOAnswerBenchSource | 88.4% | — | Not comparable |
| ApexSource | 33.0% | — | Not comparable |
| Apex ShortlistSource | 85.7% | — | Not comparable |
| AIME26Source | — | 99.2% | Not comparable |
| HMMT Nov 2025Source | — | 94.4% | Not comparable |
| MMAnswerBenchSource | — | 91.0% | Not comparable |
Multimodal1 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5.2 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 1340 | GLM-5.2 leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5.2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 79.2% | 73.3% | DeepSeek V4 Flash (Max) leads |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Flash (Max) or GLM-5.2?
DeepSeek V4 Flash (Max) and GLM-5.2 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 Flash (Max) or GLM-5.2?
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 55.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Flash (Max) or GLM-5.2?
DeepSeek V4 Flash (Max) has the edge for coding in this comparison, averaging 68.8 versus 62.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V4 Flash (Max) or GLM-5.2?
GLM-5.2 has the edge for math in this comparison, averaging 95.9 versus 94.8. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Flash (Max) or GLM-5.2?
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 63.8. Inside this category, AA Briefcase is the benchmark that creates the most daylight between them.
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