DeepSeek V4.1 Flash 1017 vs GLM 5.3 Flash
DeepSeek V4.1 Flash 1017 leads GLM 5.3 Flash on coding, 70.3 to 57.7 on the Vibe Coding Index, and GLM 5.3 Flash costs 1.2x less per output token ($0.50 vs $0.60 per million). Updated October 22, 2026; 37 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or GLM 5.3 Flash: which to pick
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash, though on 4 benchmarks against 6.
Pick GLM 5.3 Flash for output tokens 1.2x cheaper ($0.50 vs $0.60 per million) and work measured by Terminal-Bench 4 (Vals AI) or AA Intelligence Index, where it leads.
- Shared results
- 37
- DeepSeek V4.1 Flash 1017 only
- 2
- GLM 5.3 Flash only
- 65
- Like-for-like categories
- 7 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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 70.3 against 57.7.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 39.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1272.
Same board, both measured
General
Vals Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 61.3% against 50.9%.
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
GLM 5.3 Flash
DeepSeek V4.1 Flash 1017 $0.0004, GLM 5.3 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, GLM 5.3 Flash 57.7 from 6 benchmarks.
- Widest category
- Coding, 18.2 points apart on average over 9 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 1017 87.7% against GLM 5.3 Flash 30.8%, 57.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against GLM 5.3 Flash $0.50 per million output tokens, 1.2x 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 5.3 Flash | Ties |
|---|---|---|---|---|
| Coding | 8 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 5 | 2 | 1 tie | |
| Reasoning | 5 | 0 | ||
| Math | 3 | 0 | ||
| Web design | 6 | 2 | ||
| General | 3 | 0 | ||
| All shared boards | 30 | 6 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index81
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 80.6% | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev | 1664 | 1607 | DeepSeek V4.1 Flash 1017 |
| AA Coding Index | Not measured | 71.5 | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 80.5% | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 92.0% | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | 37.4% | GLM 5.3 Flash |
| Vibe Code Bench (Vals AI) | 87.7% | 30.8% | DeepSeek V4.1 Flash 1017 |
| Code migration (Vals AI) | 44.2% | 20.5% | DeepSeek V4.1 Flash 1017 |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 62.9% | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 16.0% | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | Not measured | 40.2% | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | Not measured | 67.9% | Only GLM 5.3 Flash measured |
| SciCode | 55.9% | 51.6% | DeepSeek V4.1 Flash 1017 |
| FrontierCode | Not measured | 31.8% | Only GLM 5.3 Flash measured |
| FrontierSWE | Not measured | 18.1% | Only GLM 5.3 Flash measured |
| ALE-Bench | 1622.0 | 303.6 | DeepSeek V4.1 Flash 1017 |
| LMArena Text: coding | Not measured | 1507 | Only GLM 5.3 Flash measured |
| LMArena Text: software and IT | Not measured | 1500 | Only GLM 5.3 Flash measured |
| LMArena WebDev: React | 1667 | 1624 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: HTML | 1687 | 1577 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: from an image | Not measured | 1578 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 41.8 | GLM 5.3 Flash |
| Epoch Capabilities Index | Not measured | 153.1 | Only GLM 5.3 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.9 | Only GLM 5.3 Flash measured |
| Design Arena: spreadsheets | 1245 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | 70.1% | DeepSeek V4.1 Flash 1017 |
| LiveBench browser | 76.0% | 84.0% | GLM 5.3 Flash |
| LiveBench long agentic | 50.9% | 28.4% | DeepSeek V4.1 Flash 1017 |
| SREBench (Vals AI) | Not measured | 0.8% | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.0% | 57.9% | GLM 5.3 Flash |
| Tax Agent Bench (Vals AI) | 44.4% | 31.6% | DeepSeek V4.1 Flash 1017 |
| Legal Research Bench (Vals AI) | 45.2% | 45.2% | Tie |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | 10.4% | DeepSeek V4.1 Flash 1017 |
| Excel Modeling Benchmark (Vals AI) | 61.8% | 55.9% | DeepSeek V4.1 Flash 1017 |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only GLM 5.3 Flash measured |
| APEX-Agents | Not measured | 52.8% | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 86.4% | Only GLM 5.3 Flash measured |
| LiveBench reasoning | 89.1% | 77.6% | DeepSeek V4.1 Flash 1017 |
| MMMU Pro (Vals AI) | Not measured | 86.0% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.2% | Only GLM 5.3 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 39.9% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | 90.2% | DeepSeek V4.1 Flash 1017 |
| ARC-AGI | Not measured | 91.0% | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | Not measured | 65.8% | Only GLM 5.3 Flash measured |
| Chess puzzles | 46.0% | 21.0% | DeepSeek V4.1 Flash 1017 |
| Mystery game puzzles | 49.0% | 8.0% | DeepSeek V4.1 Flash 1017 |
| LMArena Text: hard prompts | Not measured | 1490 | Only GLM 5.3 Flash measured |
| LMArena Text: expert | Not measured | 1511 | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1488 | Only GLM 5.3 Flash measured |
| LMArena Vision: diagrams | Not measured | 1317 | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | Not measured | 1318 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | 93.9% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tiers 1 to 3 | 71.2% | 55.8% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tier 4 | 46.3% | 17.1% | DeepSeek V4.1 Flash 1017 |
| ProofBench | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| LMArena Text: math | Not measured | 1503 | Only GLM 5.3 Flash measured |
| LMArena Text: mathematical work | Not measured | 1497 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.1% | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | Not measured | 83.9% | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | Not measured | 75.6% | Only GLM 5.3 Flash measured |
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: science | Not measured | 1484 | Only GLM 5.3 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1475 | Only GLM 5.3 Flash measured |
| LMArena Text: legal and government | Not measured | 1473 | Only GLM 5.3 Flash measured |
| LMArena Text: business and finance | Not measured | 1460 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | 1272 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1303 | 1316 | GLM 5.3 Flash |
| Design Arena: game dev | 1357 | 1278 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1330 | 1262 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1358 | 1316 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1332 | 1282 | DeepSeek V4.1 Flash 1017 |
| Design Arena: SVG | Not measured | 1285 | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1242 | 1267 | GLM 5.3 Flash |
| LiveBench design | 83.4% | 77.2% | DeepSeek V4.1 Flash 1017 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Vals Index | 61.3% | 50.9% | DeepSeek V4.1 Flash 1017 |
| LiveBench data analysis | 79.5% | 76.4% | DeepSeek V4.1 Flash 1017 |
| MedScribe (Vals AI) | Not measured | 88.9% | Only GLM 5.3 Flash measured |
| Long context reasoning (AA-LCR) | 82.7% | 80.0% | DeepSeek V4.1 Flash 1017 |
| Surface Evolver Bench | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| LMArena Text | Not measured | 1471 | Only GLM 5.3 Flash measured |
| LMArena Text: instruction following | Not measured | 1478 | Only GLM 5.3 Flash measured |
| LMArena Text: creative writing | Not measured | 1441 | Only GLM 5.3 Flash measured |
| LMArena Vision | Not measured | 1294 | Only GLM 5.3 Flash measured |
| LMArena Text: English | Not measured | 1474 | Only GLM 5.3 Flash measured |
| LMArena Text: non-English | Not measured | 1462 | Only GLM 5.3 Flash measured |
| LMArena Text: Chinese | Not measured | 1531 | Only GLM 5.3 Flash measured |
| LMArena Text: German | Not measured | 1462 | Only GLM 5.3 Flash measured |
| LMArena Text: Japanese | Not measured | 1444 | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | Not measured | 1436 | Only GLM 5.3 Flash measured |
| LMArena Text: French | Not measured | 1491 | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | Not measured | 1475 | Only GLM 5.3 Flash measured |
| LMArena Text: Russian | Not measured | 1465 | Only GLM 5.3 Flash measured |
| LMArena Text: Polish | Not measured | 1450 | Only GLM 5.3 Flash measured |
| LMArena Text: multi-turn | Not measured | 1469 | Only GLM 5.3 Flash measured |
| LMArena Text: longer queries | Not measured | 1481 | Only GLM 5.3 Flash measured |
| LMArena Text: writing and language | Not measured | 1455 | Only GLM 5.3 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1440 | Only GLM 5.3 Flash measured |
| LMArena Vision: English | Not measured | 1295 | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | Not measured | 1363 | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | Not measured | 1299 | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | Not measured | 1279 | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | Not measured | 1270 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 5.3 Flash | 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
GLM 5.3 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 1017 | Z.ai GLM 5.3 Flash |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | August 26, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.15 |
| Output $/Mtok | $0.60 | $0.50 |
| Coding index | 70.3 | 57.7 |
| Independent measurements behind it | 4 | 6 |
| Cost per solved task | Not computable | $0.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than GLM 5.3 Flash for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash, though on 4 benchmarks against 6. Pick GLM 5.3 Flash for output tokens 1.2x cheaper ($0.50 vs $0.60 per million) and work measured by Terminal-Bench 4 (Vals AI) or AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GLM 5.3 Flash?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or GLM 5.3 Flash?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GLM 5.3 Flash comparison based on?
37 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and GLM 5.3 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.