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Model comparison

DeepSeek V4 Flash 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.

58.88/100
Margin
2.3pts
winning →
61.16/100
2 category wins2 category wins

Public leaderboard positions: DeepSeek V4 Flash #61 (Estimated); GLM-4.7 #42 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. DeepSeek V4 Flash and GLM-4.7 share 7 comparable benchmark results. 4 of 8 categories are comparable. 15 results are unique to DeepSeek V4 Flash; 23 to GLM-4.7.

Updated July 23, 2026
Shared results
7
DeepSeek V4 Flash only
15
GLM-4.7 only
23
Comparable categories
4 / 8

Pick GLM-4.7 if you want the stronger benchmark profile. DeepSeek V4 Flash 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 58.88. 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 38.8. The single biggest benchmark swing on the page is HLE, 8.1% to 24.8%. DeepSeek V4 Flash does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.

DeepSeek V4 Flash is also the more expensive model on tokens at $0.14 input / $0.28 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 Flash 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 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 Flash and GLM-4.7
CategoryDeepSeek V4 FlashΔGLM-4.7
MathDeepSeek V4 Flash40.8Margin← 39.0GLM-4.71.8
KnowledgeDeepSeek V4 Flash38.8Margin→ 13.0GLM-4.751.8
CodingDeepSeek V4 Flash64.2Margin→ 11.2GLM-4.775.4
AgenticDeepSeek V4 Flash49.1Margin← 3.4GLM-4.745.7

Decisive benchmark drivers

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

More
A · DeepSeek V4 FlashB · GLM-4.7

  1. HLE

    Knowledge
    Source ↗
    A 8.1%B 24.8%
    Winner: GLM-4.7Δ 16.7
    HLE: DeepSeek V4 Flash scored 8.1%; GLM-4.7 scored 24.8%. GLM-4.7 wins this benchmark.

  2. GPQA

    Knowledge
    Source ↗
    A 71.2%B 85.7%
    Winner: GLM-4.7Δ 14.5
    GPQA: DeepSeek V4 Flash scored 71.2%; GLM-4.7 scored 85.7%. GLM-4.7 wins this benchmark.

  3. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 49.1%B 41%
    Winner: DeepSeek V4 FlashΔ 8.1
    Terminal-Bench 2.0: DeepSeek V4 Flash scored 49.1%; GLM-4.7 scored 41%. DeepSeek V4 Flash wins this benchmark.

  4. MMLU-Pro

    Knowledge
    Source ↗
    A 83%B 84.3%
    Winner: GLM-4.7Δ 1.3
    MMLU-Pro: DeepSeek V4 Flash scored 83%; GLM-4.7 scored 84.3%. GLM-4.7 wins this benchmark.

  5. SWE-bench Verified

    Coding
    Source ↗
    A 73.7%B 73.8%
    Winner: GLM-4.7Δ 0.1
    SWE-bench Verified: DeepSeek V4 Flash scored 73.7%; 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 FlashGLM-4.7Comparison
Input / output priceUSD per 1M tokensDeepSeek V4 Flash$0.14 input / $0.28 outputGLM-4.7$0 input / $0 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondDeepSeek V4 FlashNot availableGLM-4.782 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenDeepSeek V4 FlashNot availableGLM-4.71.10 sA complete latency comparison is not available.
Context windowmaximum listed tokensDeepSeek V4 Flash1MGLM-4.7200KDeepSeek V4 Flash lists the larger context window.

Benchmark Deep Dive

AgenticDeepSeek V4 Flash wins
BenchmarkDeepSeek V4 FlashGLM-4.7Result
Terminal-Bench 2.0Source 49.1%41%DeepSeek V4 Flash leads
MCP AtlasSource 64%—Not comparable
ToolathlonSource 40.7%—Not comparable
Claw-EvalSource 57.8%—Not comparable
Gert LabsSource 54.35%39.95%DeepSeek V4 Flash leads
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 FlashGLM-4.7Result
SWE-bench VerifiedSource 73.7%73.8%GLM-4.7 leads
SWE-bench ProSource 49.1%—Not comparable
SWE MultilingualSource 69.7%—Not comparable
Terminal-Bench 2.0Source 49.1%—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 FlashGLM-4.7Result
MRCR 1MSource 37.5%—Not comparable
CorpusQA 1MSource 15.5%—Not comparable
AA-LCRSource —64.0%Not comparable
CritPtSource —1.7%Not comparable
KnowledgeGLM-4.7 wins
BenchmarkDeepSeek V4 FlashGLM-4.7Result
MMLU-ProSource 83%84.3%GLM-4.7 leads
SimpleQASource 23.1%—Not comparable
Chinese-SimpleQASource 71.5%—Not comparable
GPQASource 71.2%85.7%GLM-4.7 leads
GPQA-DSource 71.2%—Not comparable
HLESource 8.1%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 Flash wins
BenchmarkDeepSeek V4 FlashGLM-4.7Result
HMMT Feb 2026Source 40.8%—Not comparable
IMOAnswerBenchSource 41.9%—Not comparable
ApexSource 1.0%—Not comparable
Apex ShortlistSource 9.3%—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 FlashGLM-4.7Result
Design Arena WebsiteSource 12381255GLM-4.7 leads
Inst. Following
BenchmarkDeepSeek V4 FlashGLM-4.7Result
AA-IFBenchSource —67.9%Not comparable
Frequently Asked Questions (5)

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

GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 58.88. The biggest single separator in this matchup is HLE, where the scores are 8.1% and 24.8%.

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

GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 51.8 versus 38.8. Inside this category, HLE is the benchmark that creates the most daylight between them.

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

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 64.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

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

DeepSeek V4 Flash has the edge for math in this comparison, averaging 40.8 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 Flash or GLM-4.7?

DeepSeek V4 Flash has the edge for agentic tasks in this comparison, averaging 49.1 versus 45.7. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.

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