Agentic
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- 59.1
- Supported · #28/152
- Basis
- BenchAlign lane · 8 vs 6 public rows
- Reading
- Not comparable
DeepSeek V4.1 Flash vs GLM-5.2
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V4.1 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
16 DeepSeek V4.1 Flash only
5 Shared
20 GLM-5.2 only
- Shared results
- 5
- DeepSeek V4.1 Flash only
- 16
- GLM-5.2 only
- 20
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- 61.0
- Supported · #19/151
- Basis
- BenchAlign lane · 6 vs 8 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- 60.7
- Supported · #35/182
- Basis
- BenchAlign lane · 3 vs 6 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GLM-5.2
- 89.8
- #22/121
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4.1 Flash | GLM-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 59.1Supported · #28/152 | Not comparableBenchAlign lane · 8 vs 6 public rows | Not comparable |
| Coding | Not ranked | 61.0Supported · #19/151 | Not comparableBenchAlign lane · 6 vs 8 public rows | Not comparable |
| Reasoning | Not ranked | 74.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 60.7Supported · #35/182 | Not comparableBenchAlign lane · 3 vs 6 public rows | Not comparable |
| Math | Not ranked | 80.7Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 89.8#22/121 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
HLE
Knowledge
DeepSeek V4.1 Flash: 36.8%GLM-5.2: 54.7%Normalized gap 17.9DeepSeek V4.1 Flash source GLM-5.2 sourceGPQA
Knowledge
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 V4.1 Flash
- $0.0009
- Fits in one request
- GLM-5.2
- $0.0036
- Fits in one request
DeepSeek V4.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4.1 Flash
- $0.0186
- Fits in one request
- GLM-5.2
- $0.0832
- Fits in one request
DeepSeek V4.1 Flash 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 V4.1 Flash
- $0.0192
- Fits in one request
- GLM-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
DeepSeek V4.1 Flash has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4.1 FlashGLM-5.2
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4.1 Flash
GLM-5.2
1M
API model ID
DeepSeek V4.1 Flash
deepseek-flash
DeepSeek models and pricingGLM-5.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4.1 Flash
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingGLM-5.2
Not published
Documented inputs
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideGLM-5.2
Not sourced
Documented outputs
DeepSeek V4.1 Flash
GLM-5.2
Not sourced
Provider availability
DeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseGLM-5.2
Not sourced
Reasoning profile
DeepSeek V4.1 Flash
Reasoning
GLM-5.2
Reasoning
Weight access
DeepSeek V4.1 Flash
Open Weight
GLM-5.2
Open Weight
License
DeepSeek V4.1 Flash
Open Weight
GLM-5.2
Open Weight
Release date
DeepSeek V4.1 Flash
2026-09-10
GLM-5.2
2026-06-16
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0186 vs $0.0832. Cache-heavy agent loop: $0.0192 vs $0.352.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceGLM-5.2—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceGLM-5.2—Not directly comparable
Terminal-Bench 4.0
DeepSeek V4.1 Flash31.20%SourceGLM-5.2—Not directly comparable
CyberGym
DeepSeek V4.1 Flash88.1%SourceGLM-5.2—Not directly comparable
ExploitGym
DeepSeek V4.1 Flash15.3%SourceGLM-5.2—Not directly comparable
HLE w/ tools
DeepSeek V4.1 Flash63.9%SourceGLM-5.2—Not directly comparable
AutomationBench
DeepSeek V4.1 Flash54.8%SourceGLM-5.2—Not directly comparable
Agents' Last Exam
DeepSeek V4.1 Flash31.8%SourceGLM-5.2—Not directly comparable
Terminal-Bench 3.0
DeepSeek V4.1 Flash—GLM-5.24.6%SourceNot directly comparable
Terminal-Bench 2.0
DeepSeek V4.1 Flash—GLM-5.281%SourceNot directly comparable
MCP Atlas
DeepSeek V4.1 Flash—GLM-5.276.8%SourceNot directly comparable
Toolathlon
DeepSeek V4.1 Flash—GLM-5.248.2%SourceNot directly comparable
ResearchClawBench
DeepSeek V4.1 Flash—GLM-5.220.7%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4.1 Flash—GLM-5.267.8%SourceNot directly comparable
Coding
Codeforces
DeepSeek V4.1 Flash3471.0SourceGLM-5.2—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceGLM-5.2—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceGLM-5.2—Not directly comparable
DeepSWE
DeepSeek V4.1 Flash74.2%SourceGLM-5.2—Not directly comparable
ProgramBench
DeepSeek V4.1 Flash20.3%SourceGLM-5.263.7%SourceGLM-5.2 leads this result
NL2Repo
DeepSeek V4.1 Flash65.4%SourceGLM-5.248.9%SourceDeepSeek V4.1 Flash leads this result
SWE-bench Pro
DeepSeek V4.1 Flash—GLM-5.262.1%SourceNot directly comparable
Terminal-Bench 2.0
DeepSeek V4.1 Flash—GLM-5.281.0%SourceNot directly comparable
cursorBench32
DeepSeek V4.1 Flash—GLM-5.255.0%SourceNot directly comparable
OpenHarmony Bench
DeepSeek V4.1 Flash—GLM-5.258.4%SourceNot directly comparable
LiveCodeBench (Vals)
DeepSeek V4.1 Flash—GLM-5.269.5%SourceNot directly comparable
SWE-bench (Vals)
DeepSeek V4.1 Flash—GLM-5.282.8%SourceNot directly comparable
Reasoning
CritPt
DeepSeek V4.1 Flash—GLM-5.220.9%SourceNot directly comparable
Knowledge
GPQA
DeepSeek V4.1 Flash90.9%SourceGLM-5.291.2%SourceGLM-5.2 leads this result
GPQA-D
DeepSeek V4.1 Flash90.9%SourceGLM-5.291.2%SourceGLM-5.2 leads this result
HLE
DeepSeek V4.1 Flash36.8%SourceGLM-5.254.7%SourceGLM-5.2 leads this result
HLE w/o tools
DeepSeek V4.1 Flash—GLM-5.240.5%SourceNot directly comparable
GPQA Diamond (Vals)
DeepSeek V4.1 Flash—GLM-5.285.6%SourceNot directly comparable
MMLU-Pro (Vals)
DeepSeek V4.1 Flash—GLM-5.286.7%SourceNot directly comparable
Math
Apex
DeepSeek V4.1 Flash65.6%SourceGLM-5.2—Not directly comparable
AIME26
DeepSeek V4.1 Flash—GLM-5.299.2%SourceNot directly comparable
HMMT Nov 2025
DeepSeek V4.1 Flash—GLM-5.294.4%SourceNot directly comparable
HMMT Feb 2026
DeepSeek V4.1 Flash—GLM-5.292.5%SourceNot directly comparable
MMAnswerBench
DeepSeek V4.1 Flash—GLM-5.291.0%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, DeepSeek V4.1 Flash or GLM-5.2?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, DeepSeek V4.1 Flash or GLM-5.2?
DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek V4.1 Flash or GLM-5.2?
DeepSeek V4.1 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V4.1 Flash or GLM-5.2?
For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash and $0.0036 on GLM-5.2; repository review costs $0.0186 and $0.0832; the cache-heavy agent loop costs $0.0192 and $0.352. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, DeepSeek V4.1 Flash or GLM-5.2?
Both models list the same context window, 1M.
Related comparisons
Last updated September 10, 2026
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