Coding work
Code generation, repair, and software-engineering tasks
GLM-5.1
GLM-5.1 leads on the public coding lane, 50.9 to 50.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
DeepSeek V4 Pro 0813 vs GLM-5.1
Updated September 27, 2026. Rank says DeepSeek V4 Pro 0813 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 57.14, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 16 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 16
- DeepSeek V4 Pro 0813 only
- 26
- GLM-5.1 only
- 10
- Like-for-like categories
- 1 / 8
Estimated: DeepSeek V4 Pro 0813 · Supported: GLM-5.1How the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
50.3DeepSeek V4 Pro 081350.9GLM-5.1
Like-for-like · BenchAlign v5.7
GLM-5.1 leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
26 DeepSeek V4 Pro 0813 only
16 Shared
10 GLM-5.1 only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
BrowseCompAgentic
Normalized gap 15.4DeepSeek V4 Pro 0813:83.4%GLM-5.1:68%HMMT Feb 2026Math
Normalized gap 12.6DeepSeek V4 Pro 0813:95.2%GLM-5.1:82.6%HLEKnowledge
Normalized gap 9.6DeepSeek V4 Pro 0813:42.7%GLM-5.1:52.3%LiveCodeBench (Vals)Coding
Normalized gap 6.1DeepSeek V4 Pro 0813:87.5%GLM-5.1:81.4%Terminal-Bench 2.0Agentic
Normalized gap 4.4DeepSeek V4 Pro 0813:67.9%GLM-5.1:63.5%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Like-for-like- DeepSeek V4 Pro 0813
- 50.3
- Supported · #39/135
- GLM-5.1
- 50.9
- Supported · #37/135
- Basis
- BenchAlign v5.7 lane · 15 vs 7 public rows
- Reading
- GLM-5.1 leads · intervals overlap
Agentic
Directional only- DeepSeek V4 Pro 0813
- 55.0
- Supported · #29/105
- GLM-5.1
- 41.9
- Estimated · #43/105
- Basis
- BenchAlign v5.7 lane · 11 vs 9 public rows
- Reading
- Directional only
Knowledge
Directional only- DeepSeek V4 Pro 0813
- 63.4
- Estimated · #29/158
- GLM-5.1
- 52.3
- Supported · #51/158
- Basis
- BenchAlign v5.7 lane · 8 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V4 Pro 0813
- 56.9
- Unranked · 4 rankable rows
- GLM-5.1
- 72.7
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- GLM-5.1
- 92.4
- #4/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4 Pro 0813
- 80.2
- Unranked · 4 rankable rows
- GLM-5.1
- 63.8
- #3/7
- Basis
- Provisional lane · 1 vs 4 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4 Pro 0813 | GLM-5.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 50.3Supported · #39/135 | 50.9Supported · #37/135 | Like-for-likeBenchAlign v5.7 lane · 15 vs 7 public rows | GLM-5.1 leads · intervals overlap |
| Agentic | 55.0Supported · #29/105 | 41.9Estimated · #43/105 | Directional onlyBenchAlign v5.7 lane · 11 vs 9 public rows | Directional only |
| Knowledge | 63.4Estimated · #29/158 | 52.3Supported · #51/158 | Directional onlyBenchAlign v5.7 lane · 8 vs 4 public rows | Directional only |
| Reasoning | 56.9Unranked · 4 rankable rows | 72.7Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.4#4/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 80.2Unranked · 4 rankable rows | 63.8#3/7 | Not comparableProvisional lane · 1 vs 4 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- DeepSeek V4 Pro 0813
- $0.0033
- Fits in one request
- GLM-5.1
- $0.0036
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Pro 0813
- $0.07788
- Fits in one request
- GLM-5.1
- $0.0832
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V4 Pro 0813
- $0.0748
- Fits in one request
- GLM-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4 Pro 0813GLM-5.1
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
GLM-5.1
203K
API model ID
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingGLM-5.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4 Pro 0813
$0.044 per 1M cached input tokens
DeepSeek: Models & PricingGLM-5.1
Not published
Documented inputs
DeepSeek V4 Pro 0813
GLM-5.1
Not sourced
Documented outputs
DeepSeek V4 Pro 0813
GLM-5.1
Not sourced
Provider availability
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteGLM-5.1
Not sourced
Reasoning profile
DeepSeek V4 Pro 0813
Reasoning
GLM-5.1
Reasoning
Weight access
DeepSeek V4 Pro 0813
Open Weight
GLM-5.1
Open Weight
License
DeepSeek V4 Pro 0813
Open Weight
GLM-5.1
Open Weight
Release date
DeepSeek V4 Pro 0813
2026-08-13
GLM-5.1
2026-04-07
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 57.14, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.07788 vs $0.0832. Cache-heavy agent loop: $0.0748 vs $0.352.
- Context tradeoff
- DeepSeek V4 Pro 0813 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V4 Pro 0813 or GLM-5.1?
DeepSeek V4 Pro 0813 has the higher public score estimate, 63.48 versus 57.14, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, DeepSeek V4 Pro 0813 or GLM-5.1?
GLM-5.1 leads the public coding lane, 50.9 to 50.3, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, DeepSeek V4 Pro 0813 or GLM-5.1?
DeepSeek V4 Pro 0813 scores higher for agentic tasks on the public lane, 55 to 41.9. GLM-5.1 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, DeepSeek V4 Pro 0813 or GLM-5.1?
For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.0036 on GLM-5.1; repository review costs $0.07788 and $0.0832; the cache-heavy agent loop costs $0.0748 and $0.352. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, DeepSeek V4 Pro 0813 or GLM-5.1?
DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 203K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek V4 Pro 0813
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence52 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceGLM-5.163.5%SourceDeepSeek V4 Pro 0813 leads this result
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceGLM-5.1—Not directly comparable
BrowseComp
DeepSeek V4 Pro 081383.4%SourceGLM-5.168%SourceDeepSeek V4 Pro 0813 leads this result
HLE w/ tools
DeepSeek V4 Pro 081360.0%SourceGLM-5.1—Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%SourceGLM-5.171.8%SourceDeepSeek V4 Pro 0813 leads this result
Toolathlon
DeepSeek V4 Pro 081351.8%SourceGLM-5.1—Not directly comparable
CyberGym
DeepSeek V4 Pro 081383.3%SourceGLM-5.168.7%SourceDeepSeek V4 Pro 0813 leads this result
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%SourceGLM-5.1—Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro 081325.7%SourceGLM-5.1—Not directly comparable
AutomationBench
DeepSeek V4 Pro 081331.8%SourceGLM-5.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 081354.7%SourceGLM-5.156.9%SourceGLM-5.1 leads this result
τ³-bench results
DeepSeek V4 Pro 0813—GLM-5.170.6%SourceNot directly comparable
Claw-Eval
DeepSeek V4 Pro 0813—GLM-5.162.3%SourceNot directly comparable
Gert Labs
DeepSeek V4 Pro 0813—GLM-5.160.11%SourceNot directly comparable
ResearchClawBench
DeepSeek V4 Pro 0813—GLM-5.118.2%SourceNot directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%SourceGLM-5.1—Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0SourceGLM-5.1—Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%SourceGLM-5.1—Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro 081355.4%SourceGLM-5.158.4%SourceGLM-5.1 leads this result
SWE Multilingual
DeepSeek V4 Pro 081376.2%SourceGLM-5.1—Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceGLM-5.1—Not directly comparable
Vibe Code Bench
Shared sourceDeepSeek V4 Pro 081349.93%GLM-5.131.46%DeepSeek V4 Pro 0813 leads this result
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceGLM-5.1—Not directly comparable
NL2Repo
DeepSeek V4 Pro 081361.5%SourceGLM-5.142.7%SourceDeepSeek V4 Pro 0813 leads this result
DeepSWE
DeepSeek V4 Pro 081362.7%SourceGLM-5.1—Not directly comparable
DSBench-FullStack
DeepSeek V4 Pro 081371.1%SourceGLM-5.1—Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%SourceGLM-5.1—Not directly comparable
OpenHarmony Bench
Shared sourceDeepSeek V4 Pro 081359.0%GLM-5.152.3%DeepSeek V4 Pro 0813 leads this result
LiveCodeBench (Vals)
DeepSeek V4 Pro 081387.5%SourceGLM-5.181.4%SourceDeepSeek V4 Pro 0813 leads this result
SWE-bench (Vals)
DeepSeek V4 Pro 081396.4%SourceGLM-5.176.4%SourceDeepSeek V4 Pro 0813 leads this result
SWE-Rebench
DeepSeek V4 Pro 0813—GLM-5.162.7%SourceNot directly comparable
Reasoning
Knowledge
MMLU-Pro
DeepSeek V4 Pro 081387.5%SourceGLM-5.1—Not directly comparable
SimpleQA
DeepSeek V4 Pro 081357.9%SourceGLM-5.1—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%SourceGLM-5.1—Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%SourceGLM-5.1—Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%SourceGLM-5.186.2%SourceDeepSeek V4 Pro 0813 leads this result
HLE
DeepSeek V4 Pro 081342.7%SourceGLM-5.152.3%SourceGLM-5.1 leads this result
GPQA Diamond (Vals)
DeepSeek V4 Pro 081392.4%SourceGLM-5.184.5%SourceDeepSeek V4 Pro 0813 leads this result
MMLU-Pro (Vals)
DeepSeek V4 Pro 081387.0%SourceGLM-5.186.9%SourceDeepSeek V4 Pro 0813 leads this result
Math
HMMT Feb 2026
DeepSeek V4 Pro 081395.2%SourceGLM-5.182.6%SourceDeepSeek V4 Pro 0813 leads this result
IMOAnswerBench
DeepSeek V4 Pro 081389.8%SourceGLM-5.1—Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%SourceGLM-5.1—Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%SourceGLM-5.1—Not directly comparable
AIME26
DeepSeek V4 Pro 0813—GLM-5.195.3%SourceNot directly comparable
HMMT Nov 2025
DeepSeek V4 Pro 0813—GLM-5.194.0%SourceNot directly comparable
MMAnswerBench
DeepSeek V4 Pro 0813—GLM-5.183.8%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
DeepSeek V4 Pro 0813—GLM-5.133.448%SourceNot directly comparable
FrontierMath v2 (Tier 4)
DeepSeek V4 Pro 0813—GLM-5.112.500%SourceNot directly comparable
52 public results · 16 shared
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Last updated September 27, 2026