DeepSeek V4.1 Flash vs GLM 5.2
DeepSeek V4.1 Flash leads GLM 5.2 on coding, 62.5 to 61.1 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 5.6x less per output token ($0.78 vs $4.40 per million). Updated October 21, 2026; 72 benchmarks have a result for both.
DeepSeek V4.1 Flash or GLM 5.2: which to pick
DeepSeek V4.1 Flash and GLM 5.2 are level for coding, 62.5 against 61.1 on the Vibe Coding Index, so price and context decide.
Pick GLM 5.2 for work measured by AA Intelligence Index or Excel Modeling Benchmark (Vals AI), where it leads. Pick DeepSeek V4.1 Flash for output tokens 5.6x cheaper ($0.78 vs $4.40 per million) and work measured by LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.
- Shared results
- 72
- DeepSeek V4.1 Flash only
- 29
- GLM 5.2 only
- 26
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, six public coding benchmarks
No call
Level: 62.5 against 61.1, inside 2 points.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 24.7.
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
DeepSeek V4.1 Flash leads, 1309 against 1285.
Same board, both measured
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
DeepSeek V4.1 Flash $0.0006, GLM 5.2 $0.0036 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 62.5 from 5 benchmarks, GLM 5.2 61.1 from 4 benchmarks.
- Widest category
- Reasoning, 13.9 points apart on average over 9 shared benchmarks.
- Widest single gap
- ARC-AGI-2: DeepSeek V4.1 Flash 72.9% against GLM 5.2 22.8%, 50.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.78 against GLM 5.2 $4.40 per million output tokens, 5.6x 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 | Bars | GLM 5.2 | Ties |
|---|---|---|---|---|
| Coding | 12 | 0 | 1 tie | |
| Intelligence | 1 | 1 | ||
| Agentic work | 3 | 2 | ||
| Reasoning | 7 | 2 | ||
| Math | 3 | 3 | ||
| Knowledge | 4 | 3 | ||
| Web design | 8 | 0 | ||
| General | 5 | 17 | ||
| All shared boards | 43 | 28 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index120
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1604 | DeepSeek V4.1 Flash |
| 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) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | 64.0% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | 37.9% | DeepSeek V4.1 Flash |
| ProgramBench (Vals AI) | 0.5% | 0.5% | Tie |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | 67.8% | DeepSeek V4.1 Flash |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | 45.1% | DeepSeek V4.1 Flash |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| SciCode | 51.9% | 51.2% | DeepSeek V4.1 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 50.8% | Only GLM 5.2 measured |
| FrontierCode | 43.9% | 24.5% | DeepSeek V4.1 Flash |
| WeirdML | Not measured | 70.1% | Only GLM 5.2 measured |
| ALE-Bench | 1092.3 | 1047.0 | DeepSeek V4.1 Flash |
| LMArena Text: coding | 1502 | 1487 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1486 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | 1617 | DeepSeek V4.1 Flash |
| LMArena WebDev: HTML | 1612 | 1565 | DeepSeek V4.1 Flash |
| LMArena WebDev: from an image | 1606 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 33.7 | GLM 5.2 |
| Epoch Capabilities Index | 155.9 | 151.9 | DeepSeek V4.1 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | DeepSeek V4.1 Flash | 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 | 1178 | 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 |
| LiveBench research | 86.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench long agentic | 40.1% | Not measured | Only DeepSeek V4.1 Flash measured |
| SREBench (Vals AI) | 0.8% | 0.0% | DeepSeek V4.1 Flash |
| Finance Agent (Vals AI) | 53.5% | 49.7% | DeepSeek V4.1 Flash |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | 31.3% | DeepSeek V4.1 Flash |
| Excel Modeling Benchmark (Vals AI) | 57.2% | 61.5% | GLM 5.2 |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| τ²-Bench Telecom | Not measured | 99.1% | Only GLM 5.2 measured |
| APEX-Agents | 39.5% | 45.2% | GLM 5.2 |
| 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 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index72
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GLM 5.2 measured |
| LiveBench reasoning | 86.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.5% | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 41.1% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 89.8% | 91.9% | GLM 5.2 |
| ARC-AGI | 94.5% | 77.0% | DeepSeek V4.1 Flash |
| ARC-AGI-2 | 72.9% | 22.8% | DeepSeek V4.1 Flash |
| SimpleBench | Not measured | 58.8% | Only GLM 5.2 measured |
| Chess puzzles | 48.0% | 22.0% | DeepSeek V4.1 Flash |
| Mystery game puzzles | 43.0% | 19.0% | DeepSeek V4.1 Flash |
| EBR-bench | Not measured | 9.5% | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1483 | 1481 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1487 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1481 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: homework | 1307 | Not measured | Only DeepSeek V4.1 Flash measured |
| Kagi LLM Benchmark | Not measured | 62.6% | Only GLM 5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 86.4% | DeepSeek V4.1 Flash |
| FrontierMath, tiers 1 to 3 | 67.4% | 59.2% | DeepSeek V4.1 Flash |
| FrontierMath, tier 4 | 26.8% | 29.3% | GLM 5.2 |
| ProofBench | 54.0% | 35.0% | DeepSeek V4.1 Flash |
| LMArena Text: math | 1478 | 1483 | GLM 5.2 |
| LMArena Text: mathematical work | 1485 | 1489 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GLM 5.2 measured |
| LegalBench (Vals AI) | 83.3% | 84.1% | GLM 5.2 |
| 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) | 41.2% | 40.8% | DeepSeek V4.1 Flash |
| SimpleQA Verified | 42.9% | 34.2% | DeepSeek V4.1 Flash |
| LMArena Text: science | 1469 | 1489 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1484 | 1475 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1486 | GLM 5.2 |
| LMArena Text: business and finance | 1461 | 1451 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1285 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1315 | 1296 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1330 | 1268 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1290 | 1285 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 1288 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1288 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1244 | 1214 | DeepSeek V4.1 Flash |
| Design Arena: ASCII art | 1265 | 1190 | DeepSeek V4.1 Flash |
| LiveBench design | 79.2% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index517
| Benchmark | DeepSeek V4.1 Flash | GLM 5.2 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Public Benefits Bench (Vals AI) | 64.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| SAGE (Vals AI) | 47.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedScribe (Vals AI) | 85.5% | 83.5% | DeepSeek V4.1 Flash |
| IFBench | Not measured | 73.3% | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 84.0% | 78.3% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 93.6% | GLM 5.2 |
| LMCA | 47.0% | 45.8% | DeepSeek V4.1 Flash |
| Surface Evolver Bench | 46.3% | 55.6% | GLM 5.2 |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | 1470 | GLM 5.2 |
| LMArena Text: instruction following | 1473 | 1466 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1463 | GLM 5.2 |
| LMArena Vision | 1278 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: English | 1473 | 1475 | GLM 5.2 |
| LMArena Text: non-English | 1449 | 1459 | GLM 5.2 |
| LMArena Text: Chinese | 1501 | 1522 | GLM 5.2 |
| LMArena Text: German | 1470 | 1470 | GLM 5.2 |
| LMArena Text: Japanese | 1432 | 1450 | GLM 5.2 |
| LMArena Text: Korean | 1436 | 1440 | GLM 5.2 |
| LMArena Text: French | 1465 | 1479 | GLM 5.2 |
| LMArena Text: Spanish | 1459 | 1478 | GLM 5.2 |
| LMArena Text: Russian | 1467 | 1468 | GLM 5.2 |
| LMArena Text: Polish | 1457 | 1456 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1469 | GLM 5.2 |
| LMArena Text: longer queries | 1473 | 1480 | GLM 5.2 |
| LMArena Text: writing and language | 1450 | 1463 | GLM 5.2 |
| LMArena Text: entertainment and media | 1445 | 1450 | GLM 5.2 |
| LMArena Vision: English | 1288 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: Chinese | 1327 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: reading text | 1282 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: humor | 1260 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: creative writing | 1275 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | 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 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 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 | Z.ai GLM 5.2 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | June 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.21 | $1.40 |
| Output $/Mtok | $0.78 | $4.40 |
| Coding index | 62.5 | 61.1 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | Not computable | $0.86 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than GLM 5.2 for coding?
DeepSeek V4.1 Flash and GLM 5.2 are level for coding, 62.5 against 61.1 on the Vibe Coding Index, so price and context decide. Pick GLM 5.2 for work measured by AA Intelligence Index or Excel Modeling Benchmark (Vals AI), where it leads. Pick DeepSeek V4.1 Flash for output tokens 5.6x cheaper ($0.78 vs $4.40 per million) and work measured by LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, DeepSeek V4.1 Flash or GLM 5.2?
DeepSeek V4.1 Flash costs $0.21 per million input tokens and $0.78 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, DeepSeek V4.1 Flash or GLM 5.2?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash vs GLM 5.2 comparison based on?
72 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.