Model comparison
DeepSeek V4 Flash (High) 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 (High) #92 (Estimated); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Flash (High) and GLM-5 share 23 comparable benchmark results. 4 of 8 categories are comparable. 15 results are unique to DeepSeek V4 Flash (High); 26 to GLM-5.
Updated July 23, 2026- Shared results
- 23
- DeepSeek V4 Flash (High) only
- 15
- GLM-5 only
- 26
- Comparable categories
- 4 / 8
Pick GLM-5 if you want the stronger benchmark profile. DeepSeek V4 Flash (High) only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
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
GLM-5 is clearly ahead on the BenchAlign aggregate, 66.06 to 53.95. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in knowledge, where it averages 66.4 against 52.1. The single biggest benchmark swing on the page is HLE, 29.4% to 50.4%. DeepSeek V4 Flash (High) does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
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 (High). That is roughly 11.4x on output cost alone. DeepSeek V4 Flash (High) 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 (High) 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 (High) | Δ | GLM-5 |
|---|---|---|---|
| Math | DeepSeek V4 Flash (High)91.9 | Margin← 35.6 | GLM-556.3 |
| Knowledge | DeepSeek V4 Flash (High)52.1 | Margin→ 14.3 | GLM-566.4 |
| Coding | DeepSeek V4 Flash (High)68.5 | Margin← 2.2 | GLM-566.3 |
| Agentic | DeepSeek V4 Flash (High)55.3 | Margin→ 0.9 | GLM-556.2 |
| Reasoning | DeepSeek V4 Flash (High)Not measured | MarginNo overlap | GLM-560.8 |
| Multilingual | DeepSeek V4 Flash (High)Not measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | DeepSeek V4 Flash (High)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 29.4%B 50.4%Winner: GLM-5Δ 21HLE: DeepSeek V4 Flash (High) scored 29.4%; GLM-5 scored 50.4%. GLM-5 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 91.9%B 86.4%Winner: DeepSeek V4 Flash (High)Δ 5.5HMMT Feb 2026: DeepSeek V4 Flash (High) scored 91.9%; GLM-5 scored 86.4%. DeepSeek V4 Flash (High) wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 52.3%B 55.1%Winner: GLM-5Δ 2.8SWE-bench Pro: DeepSeek V4 Flash (High) scored 52.3%; GLM-5 scored 55.1%. GLM-5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 87.4%B 86%Winner: DeepSeek V4 Flash (High)Δ 1.4GPQA: DeepSeek V4 Flash (High) scored 87.4%; GLM-5 scored 86%. DeepSeek V4 Flash (High) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 78.6%B 77.8%Winner: DeepSeek V4 Flash (High)Δ 0.8SWE-bench Verified: DeepSeek V4 Flash (High) scored 78.6%; GLM-5 scored 77.8%. DeepSeek V4 Flash (High) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Flash (High) | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Flash (High)$0.14 input / $0.28 output | GLM-5$1 input / $3.2 output | DeepSeek V4 Flash (High) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Flash (High)Not available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Flash (High)Not available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Flash (High)1M | GLM-5200K | DeepSeek V4 Flash (High) lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5 wins18 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.6% | 56.2% | DeepSeek V4 Flash (High) leads |
| BrowseCompSource | 53.5% | — | Not comparable |
| HLE w/ toolsSource | 40.3% | — | Not comparable |
| MCP AtlasSource | 67.4% | 31.1% | DeepSeek V4 Flash (High) leads |
| ToolathlonSource | 43.5% | 38% | DeepSeek V4 Flash (High) leads |
| τ²-bench resultsSource | 95.6% | 98.2% | GLM-5 leads |
| AA Agentic IndexSource | 28.2% | — | Not comparable |
| GDPval-AASource | 32.4% | — | Not comparable |
| GDPval-AASource | 1147 | — | 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 (High) wins10 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GLM-5 | Result |
|---|---|---|---|
| CodeforcesSource | 2816.0 | — | Not comparable |
| SWE-bench VerifiedSource | 78.6% | 77.8% | DeepSeek V4 Flash (High) leads |
| SWE-bench ProSource | 52.3% | 55.1% | GLM-5 leads |
| SWE MultilingualSource | 70.2% | 73.3% | GLM-5 leads |
| Terminal-Bench 2.0Source | 56.6% | — | Not comparable |
| AA-SciCodeSource | 42.0% | 46.2% | GLM-5 leads |
| AA Coding IndexSource | 52.0% | — | Not comparable |
| 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 wins14 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GLM-5 | Result |
|---|---|---|---|
| MMLU-ProSource | 86.4% | 85.7% | DeepSeek V4 Flash (High) leads |
| SimpleQASource | 28.9% | — | Not comparable |
| Chinese-SimpleQASource | 73.2% | — | Not comparable |
| GPQASource | 87.4% | 86% | DeepSeek V4 Flash (High) leads |
| GPQA-DSource | 87.4% | 86.0% | DeepSeek V4 Flash (High) leads |
| HLESource | 29.4% | 50.4% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 37.5% | 39.5% | GLM-5 leads |
| AA-GPQA DiamondSource | 86.7% | 82.0% | DeepSeek V4 Flash (High) leads |
| AA-HLESource | 27.8% | 27.2% | DeepSeek V4 Flash (High) leads |
| AA-Omniscience IndexSource | -22.3% | 2.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 35.5% | 26.9% | DeepSeek V4 Flash (High) leads |
| AA-Omniscience Hallucination RateSource | 89.7% | 34.0% | GLM-5 leads |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
MathDeepSeek V4 Flash (High) wins11 benchmarks
| Benchmark | DeepSeek V4 Flash (High) | GLM-5 | Result |
|---|---|---|---|
| HMMT Feb 2026Source | 91.9% | 86.4% | DeepSeek V4 Flash (High) leads |
| IMOAnswerBenchSource | 85.1% | — | Not comparable |
| ApexSource | 19.1% | — | Not comparable |
| Apex ShortlistSource | 72.1% | — | 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 (High) | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1238 | 1278 | GLM-5 leads |
Frequently Asked Questions (5)
Which is better, DeepSeek V4 Flash (High) or GLM-5?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 53.95. The biggest single separator in this matchup is HLE, where the scores are 29.4% and 50.4%.
Which is better for knowledge tasks, DeepSeek V4 Flash (High) or GLM-5?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 52.1. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Flash (High) or GLM-5?
DeepSeek V4 Flash (High) has the edge for coding in this comparison, averaging 68.5 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 Flash (High) or GLM-5?
DeepSeek V4 Flash (High) has the edge for math in this comparison, averaging 91.9 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 (High) or GLM-5?
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 55.3. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
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