DeepSeek V4 Flash Vision Exp vs GLM 5.3
GLM 5.3 leads DeepSeek V4 Flash Vision Exp on the Artificial Analysis Intelligence Index, 44.8 to 34.8, and DeepSeek V4 Flash Vision Exp costs 3.3x less per output token ($1.32 vs $4.40 per million). Updated October 22, 2026; 14 benchmarks have a result for both.
DeepSeek V4 Flash Vision Exp or GLM 5.3: which to pick
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; DeepSeek V4 Flash Vision Exp has none, so the two cannot be ranked on coding.
Pick DeepSeek V4 Flash Vision Exp for output tokens 3.3x cheaper ($1.32 vs $4.40 per million) and work measured by LiveBench browser or LiveBench long agentic, where it leads.
- Shared results
- 14
- DeepSeek V4 Flash Vision Exp only
- 0
- GLM 5.3 only
- 86
- Like-for-like categories
- 6 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 GLM 5.3 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 34.8.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 47.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 has a Design Arena: websites result, 1295.
Directional only
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
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 Flash Vision Exp
DeepSeek V4 Flash Vision Exp $0.0011, GLM 5.3 $0.0036 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- DeepSeek V4 Flash Vision Exp not benchmarked, GLM 5.3 70.6 from 6 benchmarks.
- Widest category
- Coding, 21.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveBench browser: DeepSeek V4 Flash Vision Exp 64.0% against GLM 5.3 0.0%, 64.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash Vision Exp $1.32 against GLM 5.3 $4.40 per million output tokens, 3.3x 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 Flash Vision Exp | Bars | GLM 5.3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 2 | 2 | ||
| Reasoning | 0 | 3 | ||
| Web design | 0 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 4 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| LiveBench coding | 37.6% | 82.8% | GLM 5.3 |
| LMArena WebDev | Not measured | 1621 | Only GLM 5.3 measured |
| AA Coding Index | 65.0 | 74.8 | GLM 5.3 |
| LiveCodeBench (Vals AI) | Not measured | 80.5% | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | Not measured | 95.4% | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 38.9% | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | Not measured | 78.1% | Only GLM 5.3 measured |
| Code migration (Vals AI) | Not measured | 44.2% | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | Not measured | 1.5% | Only GLM 5.3 measured |
| IOI (Vals AI) | Not measured | 68.4% | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 71.5% | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 20.0% | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | Not measured | 47.5% | Only GLM 5.3 measured |
| CyberBench (Vals AI) | Not measured | 72.1% | Only GLM 5.3 measured |
| SciCode | 49.7% | 59.0% | GLM 5.3 |
| FrontierCode | Not measured | 40.1% | Only GLM 5.3 measured |
| FrontierSWE | Not measured | 30.2% | Only GLM 5.3 measured |
| WeirdML | Not measured | 75.4% | Only GLM 5.3 measured |
| ALE-Bench | Not measured | 1317.4 | Only GLM 5.3 measured |
| LMArena Text: coding | Not measured | 1497 | Only GLM 5.3 measured |
| LMArena Text: software and IT | Not measured | 1493 | Only GLM 5.3 measured |
| LMArena WebDev: React | Not measured | 1641 | Only GLM 5.3 measured |
| LMArena WebDev: HTML | Not measured | 1595 | Only GLM 5.3 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 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 34.8 | 44.8 | GLM 5.3 |
| Epoch Capabilities Index | Not measured | 156.0 | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| AA Agentic Index | 47.5 | 53.1 | GLM 5.3 |
| Design Arena: agents building mobile apps | Not measured | 1194 | Only GLM 5.3 measured |
| Design Arena: HTML slides | Not measured | 1189 | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | Not measured | 1230 | Only GLM 5.3 measured |
| LiveBench research | 69.2% | 73.2% | GLM 5.3 |
| LiveBench browser | 64.0% | 0.0% | DeepSeek V4 Flash Vision Exp |
| LiveBench long agentic | 39.0% | 35.9% | DeepSeek V4 Flash Vision Exp |
| SREBench (Vals AI) | Not measured | 9.2% | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | Not measured | 55.8% | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 39.7% | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | Not measured | 49.0% | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 14.6% | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.3% | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 5.7% | Only GLM 5.3 measured |
| APEX-Agents | Not measured | 56.6% | Only GLM 5.3 measured |
| Vending-Bench 2 | Not measured | $8,164 | Only GLM 5.3 measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 88.1% | Only GLM 5.3 measured |
| LiveBench reasoning | 85.4% | 85.8% | GLM 5.3 |
| MysteryMechanism (Vals AI) | Not measured | 23.0% | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.3% | 91.7% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 34.5% | 42.3% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | Not measured | 90.9% | Only GLM 5.3 measured |
| Chess puzzles | Not measured | 37.0% | Only GLM 5.3 measured |
| Mystery game puzzles | Not measured | 33.0% | Only GLM 5.3 measured |
| LMArena Text: hard prompts | Not measured | 1490 | Only GLM 5.3 measured |
| LMArena Text: expert | Not measured | 1514 | Only GLM 5.3 measured |
| LMArena Text: hard prompts in English | Not measured | 1496 | Only GLM 5.3 measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 91.1% | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 68.8% | Only GLM 5.3 measured |
| FrontierMath, tier 4 | Not measured | 29.3% | Only GLM 5.3 measured |
| ProofBench | Not measured | 49.0% | Only GLM 5.3 measured |
| LMArena Text: math | Not measured | 1495 | Only GLM 5.3 measured |
| LMArena Text: mathematical work | Not measured | 1497 | Only GLM 5.3 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.8% | Only GLM 5.3 measured |
| LegalBench (Vals AI) | Not measured | 84.8% | Only GLM 5.3 measured |
| TaxEval (Vals AI) | Not measured | 72.4% | Only GLM 5.3 measured |
| MedCode (Vals AI) | Not measured | 42.9% | Only GLM 5.3 measured |
| SimpleQA Verified | Not measured | 41.0% | Only GLM 5.3 measured |
| LMArena Text: science | Not measured | 1498 | Only GLM 5.3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1481 | Only GLM 5.3 measured |
| LMArena Text: legal and government | Not measured | 1473 | Only GLM 5.3 measured |
| LMArena Text: business and finance | Not measured | 1459 | Only GLM 5.3 measured |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1295 | Only GLM 5.3 measured |
| Design Arena: UI components | Not measured | 1314 | Only GLM 5.3 measured |
| Design Arena: game dev | Not measured | 1311 | Only GLM 5.3 measured |
| Design Arena: data viz | Not measured | 1260 | Only GLM 5.3 measured |
| Design Arena: 3D | Not measured | 1332 | Only GLM 5.3 measured |
| Design Arena: all code categories | Not measured | 1304 | Only GLM 5.3 measured |
| Design Arena: SVG | Not measured | 1294 | Only GLM 5.3 measured |
| Design Arena: ASCII art | Not measured | 1208 | Only GLM 5.3 measured |
| LiveBench design | 70.7% | 77.3% | GLM 5.3 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 59.7% | Only GLM 5.3 measured |
| LiveBench data analysis | 79.5% | 70.2% | DeepSeek V4 Flash Vision Exp |
| Public Benefits Bench (Vals AI) | Not measured | 68.5% | Only GLM 5.3 measured |
| MedScribe (Vals AI) | Not measured | 88.8% | Only GLM 5.3 measured |
| Long context reasoning (AA-LCR) | 81.3% | 79.7% | DeepSeek V4 Flash Vision Exp |
| DTBench | Not measured | 87.7% | Only GLM 5.3 measured |
| LMCA | Not measured | 55.5% | Only GLM 5.3 measured |
| LMArena Text | Not measured | 1474 | Only GLM 5.3 measured |
| LMArena Text: instruction following | Not measured | 1479 | Only GLM 5.3 measured |
| LMArena Text: creative writing | Not measured | 1458 | Only GLM 5.3 measured |
| LMArena Text: English | Not measured | 1482 | Only GLM 5.3 measured |
| LMArena Text: non-English | Not measured | 1459 | Only GLM 5.3 measured |
| LMArena Text: Chinese | Not measured | 1532 | Only GLM 5.3 measured |
| LMArena Text: German | Not measured | 1488 | Only GLM 5.3 measured |
| LMArena Text: Japanese | Not measured | 1460 | Only GLM 5.3 measured |
| LMArena Text: Korean | Not measured | 1460 | Only GLM 5.3 measured |
| LMArena Text: French | Not measured | 1503 | Only GLM 5.3 measured |
| LMArena Text: Spanish | Not measured | 1462 | Only GLM 5.3 measured |
| LMArena Text: Russian | Not measured | 1469 | Only GLM 5.3 measured |
| LMArena Text: Polish | Not measured | 1466 | Only GLM 5.3 measured |
| LMArena Text: multi-turn | Not measured | 1472 | Only GLM 5.3 measured |
| LMArena Text: longer queries | Not measured | 1483 | Only GLM 5.3 measured |
| LMArena Text: writing and language | Not measured | 1469 | Only GLM 5.3 measured |
| LMArena Text: entertainment and media | Not measured | 1447 | Only GLM 5.3 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash Vision Exp | GLM 5.3 | 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 Flash Vision Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash Vision Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash Vision Exp 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 Flash Vision Exp | Z.ai GLM 5.3 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | August 21, 2026 | August 18, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.44 | $1.40 |
| Output $/Mtok | $1.32 | $4.40 |
| Coding index | Not benchmarked | 70.6 |
| Independent measurements behind it | 1 | 6 |
| Cost per solved task | Not computable | $0.36 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash Vision Exp better than GLM 5.3 for coding?
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; DeepSeek V4 Flash Vision Exp has none, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash Vision Exp for output tokens 3.3x cheaper ($1.32 vs $4.40 per million) and work measured by LiveBench browser or LiveBench long agentic, where it leads.
Which is cheaper, DeepSeek V4 Flash Vision Exp or GLM 5.3?
DeepSeek V4 Flash Vision Exp costs $0.44 per million input tokens and $1.32 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, DeepSeek V4 Flash Vision Exp or GLM 5.3?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Flash Vision Exp vs GLM 5.3 comparison based on?
14 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash Vision Exp's coding index rests on 1 independent benchmark and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.