DeepSeek V4.1 Flash 1017 vs GLM 5.2
Updated October 21, 2026. 20 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 83.3 on the Vibe Coding Index against 57.7 for GLM 5.2.
GLM 5.2 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 20
- DeepSeek V4.1 Flash 1017 only
- 12
- GLM 5.2 only
- 77
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 83.3 against 57.7.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1312 against 1285.
Same board, both measured
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, GLM 5.2 $0.0035 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 83.3 from 2 benchmarks, GLM 5.2 57.7 from 1 benchmark.
- Widest category
- Coding, 9.8 points apart on average over 7 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 1017 87.7% against GLM 5.2 64.0%, 23.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against GLM 5.2 $7.00 per million output tokens, 11.7x apart.
- Context window
- Both take 1.05M tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4.1 Flash 1017 | Bars | GLM 5.2 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 1 | 0 | ||
| Web design | 7 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 20 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | 1604 | DeepSeek V4.1 Flash 1017 |
| AA Coding Index | Not measured | 68.8 | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | Not measured | 69.5% | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.8% | Only GLM 5.2 measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | 64.0% | DeepSeek V4.1 Flash 1017 |
| Code migration (Vals AI) | 44.2% | 37.9% | DeepSeek V4.1 Flash 1017 |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.8% | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | Not measured | 45.1% | Only GLM 5.2 measured |
| SciCode | 55.9% | 51.2% | DeepSeek V4.1 Flash 1017 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 50.8% | Only GLM 5.2 measured |
| FrontierCode | Not measured | 24.5% | Only GLM 5.2 measured |
| WeirdML | Not measured | 70.1% | Only GLM 5.2 measured |
| ALE-Bench | 1622.0 | 1047.0 | DeepSeek V4.1 Flash 1017 |
| LMArena Text: coding | Not measured | 1487 | Only GLM 5.2 measured |
| LMArena Text: software and IT | Not measured | 1486 | Only GLM 5.2 measured |
| LMArena WebDev: React | 1667 | 1617 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: HTML | 1687 | 1565 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 33.7 | DeepSeek V4.1 Flash 1017 |
| Epoch Capabilities Index | Not measured | 151.9 | Only GLM 5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 38.4 | Only GLM 5.2 measured |
| Design Arena: agents building web apps | Not measured | 1222 | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1241 | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1160 | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1184 | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GLM 5.2 measured |
| Design Arena: agents building games | Not measured | 1206 | Only GLM 5.2 measured |
| Design Arena: HTML slides | Not measured | 1175 | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | Not measured | 1195 | Only GLM 5.2 measured |
| Design Arena: spreadsheets | 1238 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SREBench (Vals AI) | Not measured | 0.0% | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 57.0% | 49.7% | DeepSeek V4.1 Flash 1017 |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | 31.3% | DeepSeek V4.1 Flash 1017 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | 61.5% | DeepSeek V4.1 Flash 1017 |
| τ²-Bench Telecom | Not measured | 99.1% | Only GLM 5.2 measured |
| APEX-Agents | Not measured | 45.2% | Only GLM 5.2 measured |
| PostTrainBench | Not measured | 31.7% | Only GLM 5.2 measured |
| Vending-Bench 2 | Not measured | $8,314 | Only GLM 5.2 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GLM 5.2 measured |
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.5% | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 41.1% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | Not measured | 91.9% | Only GLM 5.2 measured |
| ARC-AGI | Not measured | 77.0% | Only GLM 5.2 measured |
| ARC-AGI-2 | Not measured | 22.8% | Only GLM 5.2 measured |
| SimpleBench | Not measured | 58.8% | Only GLM 5.2 measured |
| Chess puzzles | Not measured | 22.0% | Only GLM 5.2 measured |
| Mystery game puzzles | Not measured | 19.0% | Only GLM 5.2 measured |
| EBR-bench | Not measured | 9.5% | Only GLM 5.2 measured |
| LMArena Text: hard prompts | Not measured | 1481 | Only GLM 5.2 measured |
| LMArena Text: expert | Not measured | 1487 | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | Not measured | 1481 | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 86.4% | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 59.2% | Only GLM 5.2 measured |
| FrontierMath, tier 4 | Not measured | 29.3% | Only GLM 5.2 measured |
| ProofBench | Not measured | 35.0% | Only GLM 5.2 measured |
| LMArena Text: math | Not measured | 1483 | Only GLM 5.2 measured |
| LMArena Text: mathematical work | Not measured | 1489 | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GLM 5.2 measured |
| LegalBench (Vals AI) | Not measured | 84.1% | Only GLM 5.2 measured |
| CorpFin (Vals AI) | Not measured | 66.1% | Only GLM 5.2 measured |
| TaxEval (Vals AI) | Not measured | 73.3% | Only GLM 5.2 measured |
| MedCode (Vals AI) | Not measured | 40.8% | Only GLM 5.2 measured |
| SimpleQA Verified | Not measured | 34.2% | Only GLM 5.2 measured |
| LMArena Text: science | Not measured | 1489 | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1475 | Only GLM 5.2 measured |
| LMArena Text: legal and government | Not measured | 1486 | Only GLM 5.2 measured |
| LMArena Text: business and finance | Not measured | 1451 | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1312 | 1285 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1305 | 1296 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1365 | 1268 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1330 | 1285 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1368 | 1288 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1335 | 1288 | DeepSeek V4.1 Flash 1017 |
| Design Arena: SVG | Not measured | 1214 | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1244 | 1190 | DeepSeek V4.1 Flash 1017 |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| MedScribe (Vals AI) | Not measured | 83.5% | Only GLM 5.2 measured |
| IFBench | Not measured | 73.3% | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | 78.3% | DeepSeek V4.1 Flash 1017 |
| DTBench | Not measured | 93.6% | Only GLM 5.2 measured |
| LMCA | Not measured | 45.8% | Only GLM 5.2 measured |
| Surface Evolver Bench | Not measured | 55.6% | Only GLM 5.2 measured |
| LMArena Text | Not measured | 1470 | Only GLM 5.2 measured |
| LMArena Text: instruction following | Not measured | 1466 | Only GLM 5.2 measured |
| LMArena Text: creative writing | Not measured | 1463 | Only GLM 5.2 measured |
| LMArena Text: English | Not measured | 1475 | Only GLM 5.2 measured |
| LMArena Text: non-English | Not measured | 1459 | Only GLM 5.2 measured |
| LMArena Text: Chinese | Not measured | 1522 | Only GLM 5.2 measured |
| LMArena Text: German | Not measured | 1470 | Only GLM 5.2 measured |
| LMArena Text: Japanese | Not measured | 1450 | Only GLM 5.2 measured |
| LMArena Text: Korean | Not measured | 1440 | Only GLM 5.2 measured |
| LMArena Text: French | Not measured | 1479 | Only GLM 5.2 measured |
| LMArena Text: Spanish | Not measured | 1478 | Only GLM 5.2 measured |
| LMArena Text: Russian | Not measured | 1468 | Only GLM 5.2 measured |
| LMArena Text: Polish | Not measured | 1456 | Only GLM 5.2 measured |
| LMArena Text: multi-turn | Not measured | 1469 | Only GLM 5.2 measured |
| LMArena Text: longer queries | Not measured | 1480 | Only GLM 5.2 measured |
| LMArena Text: writing and language | Not measured | 1463 | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | Not measured | 1450 | Only GLM 5.2 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.2 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4.1 Flash 1017 | Z.ai GLM 5.2 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | June 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.0341 |
| Output $/Mtok | $0.60 | $7.00 |
| Coding index | 83.3 | 57.7 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $0.86 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than GLM 5.2 for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 83.3 on the Vibe Coding Index against 57.7 for GLM 5.2. GLM 5.2 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GLM 5.2?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; GLM 5.2 costs $0.0341 and $7.00. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or GLM 5.2?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GLM 5.2 comparison based on?
20 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 2 independent benchmarks and GLM 5.2's on 1. Where only one model has a result, the page says so instead of naming a winner.