DeepSeek V4.1 Flash 1017 vs GLM 4.5V
DeepSeek V4.1 Flash 1017 leads GLM 4.5V on the Artificial Analysis Intelligence Index, 39.5 to 7.6, and DeepSeek V4.1 Flash 1017 costs 3.0x less per output token ($0.60 vs $1.80 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or GLM 4.5V: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 3
- DeepSeek V4.1 Flash 1017 only
- 36
- GLM 4.5V only
- 38
- Like-for-like categories
- 3 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
Only DeepSeek V4.1 Flash 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 7.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash 1017 has a Design Arena: websites result, 1312.
Directional only
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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, GLM 4.5V $0.0015 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, GLM 4.5V not benchmarked.
- Widest category
- General, 82.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): DeepSeek V4.1 Flash 1017 82.7% against GLM 4.5V 0.0%, 82.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against GLM 4.5V $1.80 per million output tokens, 3.0x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M tokens, GLM 4.5V 66K.
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 4.5V | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 60.4% | Only GLM 4.5V measured |
| SciCode | 55.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.8% | Only GLM 4.5V measured |
| ALE-Bench | 1622.0 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: coding | Not measured | 1347 | Only GLM 4.5V measured |
| LMArena Text: software and IT | Not measured | 1353 | Only GLM 4.5V measured |
| LMArena WebDev: React | 1667 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1687 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 7.6 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | 1230 | 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 |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| 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% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| τ²-Bench Telecom | Not measured | 22.5% | Only GLM 4.5V measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 68.4% | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 6.3% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: hard prompts | Not measured | 1335 | Only GLM 4.5V measured |
| LMArena Text: expert | Not measured | 1353 | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | Not measured | 1357 | Only GLM 4.5V measured |
| LMArena Vision: diagrams | Not measured | 1151 | Only GLM 4.5V measured |
| LMArena Vision: homework | Not measured | 1163 | Only GLM 4.5V measured |
| Kagi LLM Benchmark | Not measured | 59.8% | Only GLM 4.5V measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 73.0 | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only GLM 4.5V measured |
| OTIS Mock AIME | 99.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tiers 1 to 3 | 71.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: math | Not measured | 1354 | Only GLM 4.5V measured |
| LMArena Text: mathematical work | Not measured | 1336 | Only GLM 4.5V measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 78.8% | Only GLM 4.5V measured |
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: science | Not measured | 1330 | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | Not measured | 1349 | Only GLM 4.5V measured |
| LMArena Text: legal and government | Not measured | 1338 | Only GLM 4.5V measured |
| LMArena Text: business and finance | Not measured | 1309 | Only GLM 4.5V measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | Leader |
|---|---|---|---|
| Design Arena: websites | 1312 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: UI components | 1300 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1362 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1335 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1372 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1334 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: ASCII art | 1243 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | 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 |
| IFBench | Not measured | 34.2% | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 82.7% | 0.0% | DeepSeek V4.1 Flash 1017 |
| LMArena Text | Not measured | 1333 | Only GLM 4.5V measured |
| LMArena Text: instruction following | Not measured | 1311 | Only GLM 4.5V measured |
| LMArena Text: creative writing | Not measured | 1294 | Only GLM 4.5V measured |
| LMArena Vision | Not measured | 1154 | Only GLM 4.5V measured |
| LMArena Text: English | Not measured | 1362 | Only GLM 4.5V measured |
| LMArena Text: non-English | Not measured | 1303 | Only GLM 4.5V measured |
| LMArena Text: Chinese | Not measured | 1337 | Only GLM 4.5V measured |
| LMArena Text: Spanish | Not measured | 1336 | Only GLM 4.5V measured |
| LMArena Text: Russian | Not measured | 1298 | Only GLM 4.5V measured |
| LMArena Text: Polish | Not measured | 1302 | Only GLM 4.5V measured |
| LMArena Text: multi-turn | Not measured | 1333 | Only GLM 4.5V measured |
| LMArena Text: longer queries | Not measured | 1305 | Only GLM 4.5V measured |
| LMArena Text: writing and language | Not measured | 1302 | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | Not measured | 1303 | Only GLM 4.5V measured |
| LMArena Vision: English | Not measured | 1183 | Only GLM 4.5V measured |
| LMArena Vision: reading text | Not measured | 1151 | Only GLM 4.5V measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | GLM 4.5V | 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 4.5V |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | August 11, 2025 |
| Context window | 1.05M | 66K |
| Input $/Mtok | $0.15 | $0.60 |
| Output $/Mtok | $0.60 | $1.80 |
| Coding index | 70.3 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than GLM 4.5V for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GLM 4.5V?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or GLM 4.5V?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 66K.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GLM 4.5V comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.