Model comparison
DeepSeek V4 Flash (Max) vs GLM-5
Head-to-head evidence from 23 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 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (Max) and GLM-5 share 23 comparable benchmark results. 4 of 8 categories are comparable. 22 results are unique to DeepSeek V4 Flash (Max); 26 to GLM-5.
Updated July 23, 2026- Shared results
- 23
- DeepSeek V4 Flash (Max) only
- 22
- GLM-5 only
- 26
- Comparable categories
- 4 / 8
Treat this as a split decision. DeepSeek V4 Flash (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 23 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 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 $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (Max). That is roughly 11.4x on output cost alone. DeepSeek V4 Flash (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 Flash (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 Flash (Max) | Δ | GLM-5 |
|---|---|---|---|
| Math | DeepSeek V4 Flash (Max)94.8 | Margin← 38.5 | GLM-556.3 |
| Knowledge | DeepSeek V4 Flash (Max)55.3 | Margin→ 11.1 | GLM-566.4 |
| Agentic | DeepSeek V4 Flash (Max)63.8 | Margin← 7.6 | GLM-556.2 |
| Coding | DeepSeek V4 Flash (Max)68.8 | Margin← 2.5 | GLM-566.3 |
| Reasoning | DeepSeek V4 Flash (Max)Not measured | MarginNo overlap | GLM-560.8 |
| Multilingual | DeepSeek V4 Flash (Max)Not measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | DeepSeek V4 Flash (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 34.8%B 50.4%Winner: GLM-5Δ 15.6HLE: DeepSeek V4 Flash (Max) scored 34.8%; GLM-5 scored 50.4%. GLM-5 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 94.8%B 86.4%Winner: DeepSeek V4 Flash (Max)Δ 8.4HMMT Feb 2026: DeepSeek V4 Flash (Max) scored 94.8%; GLM-5 scored 86.4%. DeepSeek V4 Flash (Max) wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.6%B 55.1%Winner: GLM-5Δ 2.5SWE-bench Pro: DeepSeek V4 Flash (Max) scored 52.6%; GLM-5 scored 55.1%. GLM-5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 88.1%B 86%Winner: DeepSeek V4 Flash (Max)Δ 2.1GPQA: DeepSeek V4 Flash (Max) scored 88.1%; GLM-5 scored 86%. DeepSeek V4 Flash (Max) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 79%B 77.8%Winner: DeepSeek V4 Flash (Max)Δ 1.2SWE-bench Verified: DeepSeek V4 Flash (Max) scored 79%; GLM-5 scored 77.8%. 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 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (Max)$0.14 input / $0.28 output | GLM-5$1 input / $3.2 output | DeepSeek V4 Flash (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (Max)Not available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (Max)Not available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (Max)1M | GLM-5200K | DeepSeek V4 Flash (Max) lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Flash (Max) wins24 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.9% | 56.2% | DeepSeek V4 Flash (Max) leads |
| BrowseCompSource | 73.2% | — | Not comparable |
| HLE w/ toolsSource | 45.1% | — | Not comparable |
| MCP AtlasSource | 69% | 31.1% | DeepSeek V4 Flash (Max) leads |
| GDPval-AASource | 1189 | — | Not comparable |
| ToolathlonSource | 47.8% | 38% | DeepSeek V4 Flash (Max) leads |
| AA Agentic IndexSource | 31.1% | — | Not comparable |
| τ²-bench resultsSource | 95% | 98.2% | GLM-5 leads |
| GDPval-AASource | 34.4% | — | Not comparable |
| AA BriefcaseSource | 831 | — | Not comparable |
| AA EnterpriseOps-GymSource | 39.6% | — | Not comparable |
| AA Harvey LABSource | 81.3% | — | Not comparable |
| AA ITBenchSource | 31.5% | — | Not comparable |
| AA Tau3 BankingSource | 22.9% | — | Not comparable |
| terminalBenchHardSource | 35.6% | — | 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 |
| APEX-Agents-AASource | — | 14.5% | Not comparable |
| Gert LabsSource | — | 50.99% | Not comparable |
CodingDeepSeek V4 Flash (Max) wins10 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5 | Result |
|---|---|---|---|
| CodeforcesSource | 3052.0 | — | Not comparable |
| SWE-bench VerifiedSource | 79% | 77.8% | DeepSeek V4 Flash (Max) leads |
| SWE-bench ProSource | 52.6% | 55.1% | GLM-5 leads |
| SWE MultilingualSource | 73.3% | 73.3% | Tie |
| Terminal-Bench 2.0Source | 56.9% | — | Not comparable |
| AA Coding IndexSource | 56.2% | — | Not comparable |
| AA-SciCodeSource | 44.9% | 46.2% | GLM-5 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 Flash (Max) | GLM-5 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.2% | 85.7% | DeepSeek V4 Flash (Max) leads |
| SimpleQASource | 34.1% | — | Not comparable |
| Chinese-SimpleQASource | 78.9% | — | Not comparable |
| GPQASource | 88.1% | 86% | DeepSeek V4 Flash (Max) leads |
| GPQA-DSource | 88.1% | 86.0% | DeepSeek V4 Flash (Max) leads |
| HLESource | 34.8% | 50.4% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 40.3% | 39.5% | DeepSeek V4 Flash (Max) leads |
| AA-GPQA DiamondSource | 89.4% | 82.0% | DeepSeek V4 Flash (Max) leads |
| AA-HLESource | 32.1% | 27.2% | DeepSeek V4 Flash (Max) leads |
| AA-Omniscience IndexSource | -22.9% | 2.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 37.2% | 26.9% | DeepSeek V4 Flash (Max) leads |
| AA-Omniscience Hallucination RateSource | 95.8% | 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 Flash (Max) wins11 benchmarks
| Benchmark | DeepSeek V4 Flash (Max) | GLM-5 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 94.8% | 86.4% | DeepSeek V4 Flash (Max) leads |
| IMOAnswerBenchSource | 88.4% | — | Not comparable |
| ApexSource | 33.0% | — | Not comparable |
| Apex ShortlistSource | 85.7% | — | 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 Flash (Max) | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 1278 | GLM-5 leads |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Flash (Max) or GLM-5?
DeepSeek V4 Flash (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 Flash (Max) or GLM-5?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 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?
DeepSeek V4 Flash (Max) has the edge for coding in this comparison, averaging 68.8 versus 66.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V4 Flash (Max) or GLM-5?
DeepSeek V4 Flash (Max) has the edge for math in this comparison, averaging 94.8 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 Flash (Max) or GLM-5?
DeepSeek V4 Flash (Max) has the edge for agentic tasks in this comparison, averaging 63.8 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
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