DeepSeek V4 Flash 0423 vs GLM 5.2
GLM 5.2 leads DeepSeek V4 Flash 0423 on the Artificial Analysis Intelligence Index, 33.7 to 24.4, and DeepSeek V4 Flash 0423 costs 15.7x less per output token ($0.28 vs $4.40 per million). Updated October 21, 2026; 49 benchmarks have a result for both.
DeepSeek V4 Flash 0423 or GLM 5.2: which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding.
Pick DeepSeek V4 Flash 0423 for output tokens 15.7x cheaper ($0.28 vs $4.40 per million).
- Shared results
- 49
- DeepSeek V4 Flash 0423 only
- 0
- GLM 5.2 only
- 49
- 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
Only GLM 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 24.4.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.2
GLM 5.2 leads, 38.4 against 26.3.
Same board, both measured
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
GLM 5.2
GLM 5.2 leads, 1285 against 1208.
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 Flash 0423
DeepSeek V4 Flash 0423 $0.0003, 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 Flash 0423 not benchmarked, GLM 5.2 61.1 from 4 benchmarks.
- Widest category
- Agentic work, 12.1 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: DeepSeek V4 Flash 0423 45.6% against GLM 5.2 70.1%, 24.5 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0423 $0.28 against GLM 5.2 $4.40 per million output tokens, 15.7x 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 0423 | Bars | GLM 5.2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 8 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 8 | ||
| General | 0 | 19 | ||
| All shared boards | 0 | 49 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | 1604 | GLM 5.2 |
| AA Coding Index | 52.0 | 68.8 | GLM 5.2 |
| 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 |
| Vibe Code Bench (Vals AI) | Not measured | 64.0% | Only GLM 5.2 measured |
| Code migration (Vals AI) | Not measured | 37.9% | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.8% | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | Not measured | 45.1% | Only GLM 5.2 measured |
| SciCode | Not measured | 51.2% | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 50.8% | Only GLM 5.2 measured |
| FrontierCode | Not measured | 24.5% | Only GLM 5.2 measured |
| WeirdML | 45.6% | 70.1% | GLM 5.2 |
| ALE-Bench | 678.2 | 1047.0 | GLM 5.2 |
| LMArena Text: coding | 1457 | 1487 | GLM 5.2 |
| LMArena Text: software and IT | 1453 | 1486 | GLM 5.2 |
| LMArena WebDev: React | 1585 | 1617 | GLM 5.2 |
| LMArena WebDev: HTML | 1558 | 1565 | GLM 5.2 |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | 33.7 | GLM 5.2 |
| Epoch Capabilities Index | 146.1 | 151.9 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 38.4 | GLM 5.2 |
| 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 |
| SREBench (Vals AI) | Not measured | 0.0% | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | Not measured | 49.7% | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.3% | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 61.5% | Only GLM 5.2 measured |
| τ²-Bench Telecom | Not measured | 99.1% | Only GLM 5.2 measured |
| APEX-Agents | Not measured | 45.2% | Only GLM 5.2 measured |
| 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 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GLM 5.2 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.5% | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 41.1% | Only GLM 5.2 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 91.9% | Only GLM 5.2 measured |
| ARC-AGI | Not measured | 77.0% | Only GLM 5.2 measured |
| ARC-AGI-2 | Not measured | 22.8% | Only GLM 5.2 measured |
| SimpleBench | 46.3% | 58.8% | GLM 5.2 |
| Chess puzzles | Not measured | 22.0% | Only GLM 5.2 measured |
| Mystery game puzzles | Not measured | 19.0% | Only GLM 5.2 measured |
| EBR-bench | Not measured | 9.5% | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1444 | 1481 | GLM 5.2 |
| LMArena Text: expert | 1441 | 1487 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1449 | 1481 | GLM 5.2 |
| Kagi LLM Benchmark | 52.2% | 62.6% | GLM 5.2 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 86.4% | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 59.2% | Only GLM 5.2 measured |
| FrontierMath, tier 4 | Not measured | 29.3% | Only GLM 5.2 measured |
| ProofBench | Not measured | 35.0% | Only GLM 5.2 measured |
| LMArena Text: math | 1426 | 1483 | GLM 5.2 |
| LMArena Text: mathematical work | 1432 | 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 index04
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GLM 5.2 measured |
| LegalBench (Vals AI) | Not measured | 84.1% | Only GLM 5.2 measured |
| 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) | Not measured | 40.8% | Only GLM 5.2 measured |
| SimpleQA Verified | Not measured | 34.2% | Only GLM 5.2 measured |
| LMArena Text: science | 1446 | 1489 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1448 | 1475 | GLM 5.2 |
| LMArena Text: legal and government | 1439 | 1486 | GLM 5.2 |
| LMArena Text: business and finance | 1425 | 1451 | GLM 5.2 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1208 | 1285 | GLM 5.2 |
| Design Arena: UI components | 1165 | 1296 | GLM 5.2 |
| Design Arena: game dev | 1197 | 1268 | GLM 5.2 |
| Design Arena: data viz | 1127 | 1285 | GLM 5.2 |
| Design Arena: 3D | 1189 | 1288 | GLM 5.2 |
| Design Arena: all code categories | 1205 | 1288 | GLM 5.2 |
| Design Arena: SVG | 1155 | 1214 | GLM 5.2 |
| Design Arena: ASCII art | 1109 | 1190 | GLM 5.2 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | DeepSeek V4 Flash 0423 | GLM 5.2 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.5% | Only GLM 5.2 measured |
| IFBench | Not measured | 73.3% | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only GLM 5.2 measured |
| DTBench | 86.4% | 93.6% | GLM 5.2 |
| LMCA | 35.9% | 45.8% | GLM 5.2 |
| Surface Evolver Bench | Not measured | 55.6% | Only GLM 5.2 measured |
| LMArena Text | 1432 | 1470 | GLM 5.2 |
| LMArena Text: instruction following | 1421 | 1466 | GLM 5.2 |
| LMArena Text: creative writing | 1403 | 1463 | GLM 5.2 |
| LMArena Text: English | 1441 | 1475 | GLM 5.2 |
| LMArena Text: non-English | 1419 | 1459 | GLM 5.2 |
| LMArena Text: Chinese | 1463 | 1522 | GLM 5.2 |
| LMArena Text: German | 1419 | 1470 | GLM 5.2 |
| LMArena Text: Japanese | 1389 | 1450 | GLM 5.2 |
| LMArena Text: Korean | 1383 | 1440 | GLM 5.2 |
| LMArena Text: French | 1439 | 1479 | GLM 5.2 |
| LMArena Text: Spanish | 1436 | 1478 | GLM 5.2 |
| LMArena Text: Russian | 1427 | 1468 | GLM 5.2 |
| LMArena Text: Polish | 1436 | 1456 | GLM 5.2 |
| LMArena Text: multi-turn | 1449 | 1469 | GLM 5.2 |
| LMArena Text: longer queries | 1434 | 1480 | GLM 5.2 |
| LMArena Text: writing and language | 1410 | 1463 | GLM 5.2 |
| LMArena Text: entertainment and media | 1400 | 1450 | GLM 5.2 |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | 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 Flash 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0423 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 0423 | Z.ai GLM 5.2 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | June 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.13 | $1.40 |
| Output $/Mtok | $0.28 | $4.40 |
| Coding index | Not benchmarked | 61.1 |
| Independent measurements behind it | 1 | 4 |
| Cost per solved task | Not computable | $0.86 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0423 better than GLM 5.2 for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 15.7x cheaper ($0.28 vs $4.40 per million).
Which is cheaper, DeepSeek V4 Flash 0423 or GLM 5.2?
DeepSeek V4 Flash 0423 costs $0.13 per million input tokens and $0.28 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 Flash 0423 or GLM 5.2?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Flash 0423 vs GLM 5.2 comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.