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Model comparison

DeepSeek V4 Pro vs GLM-4.7

Data verified

Head-to-head evidence from 7 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

60.66/100
Margin
0.5pts
winning →
61.16/100
2 category wins2 category wins

Public leaderboard positions: DeepSeek V4 Pro #46 (Supported); GLM-4.7 #42 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. DeepSeek V4 Pro and GLM-4.7 share 7 comparable benchmark results. 4 of 8 categories are comparable. 16 results are unique to DeepSeek V4 Pro; 23 to GLM-4.7.

Updated July 23, 2026
Shared results
7
DeepSeek V4 Pro only
16
GLM-4.7 only
23
Comparable categories
4 / 8

Pick GLM-4.7 if you want the stronger benchmark profile. DeepSeek V4 Pro only becomes the better choice if mathematics is the priority or you need the larger 1M context window.

Confidence note. This is a partial-evidence comparison with 7 shared benchmark results across 4 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-4.7 has the cleaner BenchAlign overall profile here, landing at 61.16 versus 60.66. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

GLM-4.7's sharpest advantage is in knowledge, where it averages 51.8 against 41.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 59.1% to 41%. DeepSeek V4 Pro does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.

DeepSeek V4 Pro is also the more expensive model on tokens at $0.43 input / $0.87 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 is the reasoning model in the pair, while DeepSeek V4 Pro 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 gives you the larger context window at 1M, compared with 200K for GLM-4.7.

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 scores and score margins for DeepSeek V4 Pro and GLM-4.7
CategoryDeepSeek V4 ProΔGLM-4.7
MathDeepSeek V4 Pro31.7Margin← 29.9GLM-4.71.8
AgenticDeepSeek V4 Pro59.1Margin← 13.4GLM-4.745.7
KnowledgeDeepSeek V4 Pro41.3Margin→ 10.5GLM-4.751.8
CodingDeepSeek V4 Pro65.3Margin→ 10.1GLM-4.775.4

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · DeepSeek V4 ProB · GLM-4.7

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 59.1%B 41%
    Winner: DeepSeek V4 ProΔ 18.1
    Terminal-Bench 2.0: DeepSeek V4 Pro scored 59.1%; GLM-4.7 scored 41%. DeepSeek V4 Pro wins this benchmark.

  2. HLE

    Knowledge
    Source ↗
    A 7.7%B 24.8%
    Winner: GLM-4.7Δ 17.1
    HLE: DeepSeek V4 Pro scored 7.7%; GLM-4.7 scored 24.8%. GLM-4.7 wins this benchmark.

  3. GPQA

    Knowledge
    Source ↗
    A 72.9%B 85.7%
    Winner: GLM-4.7Δ 12.8
    GPQA: DeepSeek V4 Pro scored 72.9%; GLM-4.7 scored 85.7%. GLM-4.7 wins this benchmark.

  4. MMLU-Pro

    Knowledge
    Source ↗
    A 82.9%B 84.3%
    Winner: GLM-4.7Δ 1.4
    MMLU-Pro: DeepSeek V4 Pro scored 82.9%; GLM-4.7 scored 84.3%. GLM-4.7 wins this benchmark.

  5. SWE-bench Verified

    Coding
    Source ↗
    A 73.6%B 73.8%
    Winner: GLM-4.7Δ 0.2
    SWE-bench Verified: DeepSeek V4 Pro scored 73.6%; GLM-4.7 scored 73.8%. GLM-4.7 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricDeepSeek V4 ProGLM-4.7Comparison
Input / output priceUSD per 1M tokensDeepSeek V4 Pro$0.435 input / $0.87 outputGLM-4.7$0 input / $0 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondDeepSeek V4 ProNot availableGLM-4.782 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenDeepSeek V4 ProNot availableGLM-4.71.10 sA complete latency comparison is not available.
Context windowmaximum listed tokensDeepSeek V4 Pro1MGLM-4.7200KDeepSeek V4 Pro lists the larger context window.

Benchmark Deep Dive

AgenticDeepSeek V4 Pro wins
BenchmarkDeepSeek V4 ProGLM-4.7Result
Terminal-Bench 2.0Source 59.1%41%DeepSeek V4 Pro leads
MCP AtlasSource 69.4%—Not comparable
ToolathlonSource 46.3%—Not comparable
Claw-EvalSource 59.8%—Not comparable
Gert LabsSource 50.28%39.95%DeepSeek V4 Pro leads
ResearchClawBenchSource 17.1%—Not comparable
BrowseCompSource —52%Not comparable
VITA-BenchSource —15.5%Not comparable
AA Agentic IndexSource —25.4%Not comparable
τ²-bench resultsSource —95.9%Not comparable
GDPval-AASource —33.3%Not comparable
GDPval-AASource —1165Not comparable
CodingGLM-4.7 wins
BenchmarkDeepSeek V4 ProGLM-4.7Result
SWE-bench VerifiedSource 73.6%73.8%GLM-4.7 leads
SWE-bench ProSource 52.1%—Not comparable
SWE MultilingualSource 69.8%—Not comparable
Terminal-Bench 2.0Source 59.1%—Not comparable
LiveCodeBenchSource —84.9%Not comparable
SWE-RebenchSource —58.7%Not comparable
AA Coding IndexSource —45.3%Not comparable
AA-SciCodeSource —45.1%Not comparable
AA LiveCodeBenchSource —89.4%Not comparable
Reasoning
BenchmarkDeepSeek V4 ProGLM-4.7Result
MRCR 1MSource 44.7%—Not comparable
CorpusQA 1MSource 35.6%—Not comparable
AA-LCRSource —64.0%Not comparable
CritPtSource —1.7%Not comparable
KnowledgeGLM-4.7 wins
BenchmarkDeepSeek V4 ProGLM-4.7Result
MMLU-ProSource 82.9%84.3%GLM-4.7 leads
SimpleQASource 45%—Not comparable
Chinese-SimpleQASource 75.8%—Not comparable
GPQASource 72.9%85.7%GLM-4.7 leads
GPQA-DSource 72.9%—Not comparable
HLESource 7.7%24.8%GLM-4.7 leads
Artificial Analysis Intelligence IndexSource —33.7%Not comparable
AA-GPQA DiamondSource —85.9%Not comparable
AA-HLESource —25.1%Not comparable
AA-Omniscience IndexSource —-34.6%Not comparable
AA-Omniscience AccuracySource —29.3%Not comparable
AA-Omniscience Hallucination RateSource —90.3%Not comparable
MathDeepSeek V4 Pro wins
BenchmarkDeepSeek V4 ProGLM-4.7Result
HMMT Feb 2026Source 31.7%—Not comparable
IMOAnswerBenchSource 35.3%—Not comparable
ApexSource 0.4%—Not comparable
Apex ShortlistSource 9.2%—Not comparable
AIME 2025Source —95.7%Not comparable
FrontierMath v2 (Tiers 1-3)Source —2.439%Not comparable
FrontierMath v2 (Tier 4)Source —0.000%Not comparable
Multimodal
BenchmarkDeepSeek V4 ProGLM-4.7Result
Design Arena WebsiteSource 12641255DeepSeek V4 Pro leads
Inst. Following
BenchmarkDeepSeek V4 ProGLM-4.7Result
AA-IFBenchSource —67.9%Not comparable
Frequently Asked Questions (5)

Which is better, DeepSeek V4 Pro or GLM-4.7?

GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 60.66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 59.1% and 41%.

Which is better for knowledge tasks, DeepSeek V4 Pro or GLM-4.7?

GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 51.8 versus 41.3. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, DeepSeek V4 Pro or GLM-4.7?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 65.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

Which is better for math, DeepSeek V4 Pro or GLM-4.7?

DeepSeek V4 Pro has the edge for math in this comparison, averaging 31.7 versus 1.8. GLM-4.7 stays close enough that the answer can still flip depending on your workload.

Which is better for agentic tasks, DeepSeek V4 Pro or GLM-4.7?

DeepSeek V4 Pro has the edge for agentic tasks in this comparison, averaging 59.1 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

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