DeepSeek V3 0324 vs GLM 4.7
GLM 4.7 leads DeepSeek V3 0324 on the Artificial Analysis Intelligence Index, 22.2 to 9.7, and DeepSeek V3 0324 costs 2.1x less per output token ($1.07 vs $2.20 per million). Updated October 22, 2026; 24 benchmarks have a result for both.
DeepSeek V3 0324 or GLM 4.7: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; DeepSeek V3 0324 has none, so the two cannot be ranked on coding.
Pick DeepSeek V3 0324 for output tokens 2.1x cheaper ($1.07 vs $2.20 per million) and work measured by MGSM (Vals AI) or CorpFin (Vals AI), where it leads.
- Shared results
- 24
- DeepSeek V3 0324 only
- 11
- GLM 4.7 only
- 57
- 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
No call
Only GLM 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7
GLM 4.7 leads, 22.2 against 9.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V3 0324 has a AA Agentic Index result, 0.8.
Directional only
Reasoning
GPQA Diamond
GLM 4.7
GLM 4.7 leads, 80.0% against 61.6%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 52.2%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.7
GLM 4.7 leads, 82.7% against 79.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.7 has a Design Arena: websites result, 1225.
Directional only
Long documents
Listed context window
GLM 4.7
GLM 4.7 takes 205K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3 0324
DeepSeek V3 0324 $0.0008, GLM 4.7 $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- DeepSeek V3 0324 not benchmarked, GLM 4.7 55.1 from 2 benchmarks.
- Widest category
- Agentic work, 48.8 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Math Index: DeepSeek V3 0324 41.0 against GLM 4.7 95.0, 54.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.07 against GLM 4.7 $2.20 per million output tokens, 2.1x apart.
- Context window
- DeepSeek V3 0324 takes 164K tokens, GLM 4.7 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 V3 0324 | Bars | GLM 4.7 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 1 | 4 | ||
| Knowledge | 2 | 4 | ||
| General | 0 | 2 | ||
| All shared boards | 3 | 21 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena WebDev | Not measured | 1435 | Only GLM 4.7 measured |
| AA Coding Index | 21.2 | 45.3 | GLM 4.7 |
| LiveCodeBench (Vals AI) | 65.5% | 82.2% | GLM 4.7 |
| SWE-bench Verified (Vals AI) | Not measured | 69.4% | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 40.5% | 89.4% | GLM 4.7 |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 31.8% | GLM 4.7 |
| Terminal-Bench | Not measured | 33.4% | Only GLM 4.7 measured |
| WeirdML | 36.1% | Not measured | Only DeepSeek V3 0324 measured |
| ALE-Bench | Not measured | 399.5 | Only GLM 4.7 measured |
| LMArena Text: coding | Not measured | 1454 | Only GLM 4.7 measured |
| LMArena Text: software and IT | Not measured | 1453 | Only GLM 4.7 measured |
| LMArena WebDev: React | Not measured | 1420 | Only GLM 4.7 measured |
| LMArena WebDev: HTML | Not measured | 1447 | Only GLM 4.7 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 22.2 | GLM 4.7 |
| Epoch Capabilities Index | Not measured | 143.6 | Only GLM 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| Design Arena: agents building full-stack apps | Not measured | 1050 | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | Not measured | 1105 | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | Not measured | 1112 | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | Not measured | 1058 | Only GLM 4.7 measured |
| τ²-Bench Telecom | 47.1% | 95.9% | GLM 4.7 |
| METR time horizons | 49.6% | Not measured | Only DeepSeek V3 0324 measured |
| Vending-Bench 2 | Not measured | $2,377 | Only GLM 4.7 measured |
| BrowseComp-Plus | Not measured | 64.7% | Only GLM 4.7 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | 80.0% | GLM 4.7 |
| GPQA Diamond (Artificial Analysis run) | 65.5% | 85.9% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 27.4% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 67.6% | 83.3% | GLM 4.7 |
| SimpleBench | 27.2% | 47.7% | GLM 4.7 |
| Chess puzzles | Not measured | 21.0% | Only GLM 4.7 measured |
| LMArena Text: hard prompts | Not measured | 1443 | Only GLM 4.7 measured |
| LMArena Text: expert | Not measured | 1424 | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | Not measured | 1452 | Only GLM 4.7 measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| AIME | 52.2% | 93.3% | GLM 4.7 |
| MATH 500 (Vals AI) | 88.6% | Not measured | Only DeepSeek V3 0324 measured |
| MGSM (Vals AI) | 91.7% | 88.2% | DeepSeek V3 0324 |
| AA Math Index | 41.0 | 95.0 | GLM 4.7 |
| AIME 2025 (Artificial Analysis run) | 41.0% | 95.0% | GLM 4.7 |
| OTIS Mock AIME | 37.8% | 83.3% | GLM 4.7 |
| ProofBench | Not measured | 6.0% | Only GLM 4.7 measured |
| MATH level 5 | 75.5% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: math | Not measured | 1424 | Only GLM 4.7 measured |
| LMArena Text: mathematical work | Not measured | 1411 | Only GLM 4.7 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | 82.7% | GLM 4.7 |
| LegalBench (Vals AI) | 77.7% | 83.4% | GLM 4.7 |
| CaseLaw (Vals AI) | Not measured | 54.9% | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 54.7% | 46.4% | DeepSeek V3 0324 |
| TaxEval (Vals AI) | 71.1% | 68.8% | DeepSeek V3 0324 |
| MedQA (Vals AI) | 82.0% | 93.7% | GLM 4.7 |
| MedCode (Vals AI) | Not measured | 32.8% | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | 85.6% | GLM 4.7 |
| SimpleQA Verified | Not measured | 32.2% | Only GLM 4.7 measured |
| LMArena Text: science | Not measured | 1457 | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | Not measured | 1469 | Only GLM 4.7 measured |
| LMArena Text: legal and government | Not measured | 1444 | Only GLM 4.7 measured |
| LMArena Text: business and finance | Not measured | 1429 | Only GLM 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1225 | Only GLM 4.7 measured |
| Design Arena: UI components | Not measured | 1196 | Only GLM 4.7 measured |
| Design Arena: game dev | Not measured | 1188 | Only GLM 4.7 measured |
| Design Arena: data viz | Not measured | 1195 | Only GLM 4.7 measured |
| Design Arena: 3D | Not measured | 1190 | Only GLM 4.7 measured |
| Design Arena: all code categories | Not measured | 1216 | Only GLM 4.7 measured |
| Design Arena: SVG | Not measured | 1142 | Only GLM 4.7 measured |
| Design Arena: ASCII art | Not measured | 1165 | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 68.6% | Only GLM 4.7 measured |
| IFBench | 41.0% | 67.9% | GLM 4.7 |
| Long context reasoning (AA-LCR) | 40.7% | 71.0% | GLM 4.7 |
| DTBench | 64.8% | Not measured | Only DeepSeek V3 0324 measured |
| LMCA | 15.5% | Not measured | Only DeepSeek V3 0324 measured |
| Fiction.LiveBench | 50.0% | Not measured | Only DeepSeek V3 0324 measured |
| Creative writing (Lech Mazur) | 77.0% | Not measured | Only DeepSeek V3 0324 measured |
| CL-bench | Not measured | 15.9% | Only GLM 4.7 measured |
| CL-bench Life | Not measured | 10.9% | Only GLM 4.7 measured |
| LMArena Text | Not measured | 1435 | Only GLM 4.7 measured |
| LMArena Text: instruction following | Not measured | 1412 | Only GLM 4.7 measured |
| LMArena Text: creative writing | Not measured | 1401 | Only GLM 4.7 measured |
| LMArena Text: English | Not measured | 1453 | Only GLM 4.7 measured |
| LMArena Text: non-English | Not measured | 1417 | Only GLM 4.7 measured |
| LMArena Text: Chinese | Not measured | 1495 | Only GLM 4.7 measured |
| LMArena Text: German | Not measured | 1426 | Only GLM 4.7 measured |
| LMArena Text: Japanese | Not measured | 1439 | Only GLM 4.7 measured |
| LMArena Text: Korean | Not measured | 1399 | Only GLM 4.7 measured |
| LMArena Text: French | Not measured | 1432 | Only GLM 4.7 measured |
| LMArena Text: Spanish | Not measured | 1434 | Only GLM 4.7 measured |
| LMArena Text: Russian | Not measured | 1423 | Only GLM 4.7 measured |
| LMArena Text: Polish | Not measured | 1435 | Only GLM 4.7 measured |
| LMArena Text: multi-turn | Not measured | 1446 | Only GLM 4.7 measured |
| LMArena Text: longer queries | Not measured | 1432 | Only GLM 4.7 measured |
| LMArena Text: writing and language | Not measured | 1411 | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | Not measured | 1400 | Only GLM 4.7 measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 0324 | GLM 4.7 | 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 V3 0324 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3 0324 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3 0324 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 V3 0324 | Z.ai GLM 4.7 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | March 24, 2025 | December 22, 2025 |
| Context window | 164K | 205K |
| Input $/Mtok | $0.27 | $0.60 |
| Output $/Mtok | $1.07 | $2.20 |
| Coding index | Not benchmarked | 55.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.29 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 0324 better than GLM 4.7 for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; DeepSeek V3 0324 has none, so the two cannot be ranked on coding. Pick DeepSeek V3 0324 for output tokens 2.1x cheaper ($1.07 vs $2.20 per million) and work measured by MGSM (Vals AI) or CorpFin (Vals AI), where it leads.
Which is cheaper, DeepSeek V3 0324 or GLM 4.7?
DeepSeek V3 0324 costs $0.27 per million input tokens and $1.07 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, DeepSeek V3 0324 or GLM 4.7?
GLM 4.7, with 205K tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs GLM 4.7 comparison based on?
24 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3 0324's coding index rests on 0 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.