DeepSeek V4.1 Flash vs GLM 4.6
Updated October 21, 2026. 43 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 43 shared
Decision reading
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 52.2 for GLM 4.6.
Pick GLM 4.6 when the benchmark that matters is LMArena Text: legal and government, where it leads.
Model ADeepSeek V4.1 FlashDeepSeek77.2/1002 independent benchmarksNo cost per solved taskModel BGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved task
- Shared results
- 43
- DeepSeek V4.1 Flash only
- 58
- GLM 4.6 only
- 23
- Like-for-like categories
- 7 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
DeepSeek V4.1 Flash scores 77.2 against 52.2.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 24.7 against 18.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1309 against 1174.
Same board, both measured
Long documents
Listed context window
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0003, GLM 4.6 $0.0016 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 77.2 from 2 benchmarks, GLM 4.6 52.2 from 2 benchmarks.
- Widest category
- Coding, 33.9 points apart on average over 6 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 84.7% against GLM 4.6 3.1%, 81.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.525 against GLM 4.6 $2.20 per million output tokens, 4.2x apart.
- Context window
- DeepSeek V4.1 Flash takes 1.05M tokens, GLM 4.6 205K.
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 4.6 | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 1 | ||
| Web design | 7 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 42 | 1 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 68.2% | Only GLM 4.6 measured |
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1341 | DeepSeek V4.1 Flash |
| AA Coding Index | Not measured | 45.8 | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | Not measured | 81.0% | Only GLM 4.6 measured |
| Terminal-Bench 4 (Vals AI) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | 3.1% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| 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% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 69.5% | Only GLM 4.6 measured |
| SciCode | 51.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 28.8% | Only GLM 4.6 measured |
| Terminal-Bench | Not measured | 24.5% | Only GLM 4.6 measured |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| ALE-Bench | 1092.3 | 340.8 | DeepSeek V4.1 Flash |
| LMArena Text: coding | 1502 | 1449 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1455 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: HTML | 1612 | 1350 | 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 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 18.5 | DeepSeek V4.1 Flash |
| Epoch Capabilities Index | 155.9 | Not measured | Only DeepSeek V4.1 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | Not measured | 1029 | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | Not measured | 1096 | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | Not measured | 1090 | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | Not measured | 1180 | Only GLM 4.6 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% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| 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 | 76.9% | Only GLM 4.6 measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 74.5% | Only GLM 4.6 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 | 78.0% | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 14.5% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI | 94.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI-2 | 72.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Chess puzzles | 48.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | 1440 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1432 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1445 | 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 |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| AIME | Not measured | 92.7% | Only GLM 4.6 measured |
| MGSM (Vals AI) | Not measured | 89.8% | Only GLM 4.6 measured |
| AA Math Index | Not measured | 86.0 | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 86.0% | Only GLM 4.6 measured |
| OTIS Mock AIME | 98.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProofBench | 54.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: math | 1478 | 1432 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1447 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.2% | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 83.3% | 79.6% | DeepSeek V4.1 Flash |
| CorpFin (Vals AI) | Not measured | 56.8% | Only GLM 4.6 measured |
| TaxEval (Vals AI) | Not measured | 66.2% | Only GLM 4.6 measured |
| MedQA (Vals AI) | Not measured | 92.2% | Only GLM 4.6 measured |
| MedCode (Vals AI) | 41.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.9% | Only GLM 4.6 measured |
| SimpleQA Verified | 42.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: science | 1469 | 1456 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1455 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1448 | GLM 4.6 |
| LMArena Text: business and finance | 1461 | 1416 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1174 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1314 | 1147 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1331 | 1151 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1290 | 1161 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 1129 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1166 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1243 | 1108 | DeepSeek V4.1 Flash |
| Design Arena: ASCII art | 1265 | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench design | 79.2% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | DeepSeek V4.1 Flash | GLM 4.6 | 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% | Not measured | Only DeepSeek V4.1 Flash measured |
| IFBench | Not measured | 43.4% | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 84.0% | 54.0% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMCA | 47.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | 1440 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1410 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1412 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: English | 1473 | 1449 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1426 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1499 | DeepSeek V4.1 Flash |
| LMArena Text: German | 1470 | 1447 | DeepSeek V4.1 Flash |
| LMArena Text: Japanese | 1432 | 1393 | DeepSeek V4.1 Flash |
| LMArena Text: Korean | 1436 | 1400 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | 1459 | DeepSeek V4.1 Flash |
| LMArena Text: Spanish | 1459 | 1436 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1418 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1447 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1427 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1422 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1415 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1409 | DeepSeek V4.1 Flash |
| 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 4.6 | 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$0.0003Fits in one request
GLM 4.6$0.0016Fits in one request
DeepSeek V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.0040Fits in one request
GLM 4.6$0.034Fits in one request
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.015Fits in one request
GLM 4.6$0.132Exceeds the context window
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 4.6 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | September 30, 2025 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $0.049 | $0.55 |
| Output $/Mtok | $0.525 | $2.20 |
| Coding index | 77.2 | 52.2 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than GLM 4.6 for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 52.2 for GLM 4.6. Pick GLM 4.6 when the benchmark that matters is LMArena Text: legal and government, where it leads.
Which is cheaper, DeepSeek V4.1 Flash or GLM 4.6?
DeepSeek V4.1 Flash costs $0.049 per million input tokens and $0.525 per million output tokens; GLM 4.6 costs $0.55 and $2.20. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash or GLM 4.6?
DeepSeek V4.1 Flash, with 1.05M tokens against 205K.
How many benchmarks is the DeepSeek V4.1 Flash vs GLM 4.6 comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 2 independent benchmarks and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.