DeepSeek V3 0324 vs GLM 4.7 Flash
GLM 4.7 Flash leads DeepSeek V3 0324 on the Artificial Analysis Intelligence Index, 14.9 to 9.7, and GLM 4.7 Flash costs 2.7x less per output token ($0.40 vs $1.07 per million). Updated October 21, 2026; 9 benchmarks have a result for both.
DeepSeek V3 0324 or GLM 4.7 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3 0324 for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 9
- DeepSeek V3 0324 only
- 26
- GLM 4.7 Flash only
- 34
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7 Flash
GLM 4.7 Flash leads, 14.9 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
No call
Only DeepSeek V3 0324 has a GPQA Diamond result, 61.6%.
Directional only
Math
AIME
No call
Only DeepSeek V3 0324 has a AIME result, 52.2%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3 0324 has a MMLU Pro result, 79.5%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
GLM 4.7 Flash
GLM 4.7 Flash takes 200K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
DeepSeek V3 0324 $0.0008, GLM 4.7 Flash $0.0003 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 0324 not benchmarked, GLM 4.7 Flash not benchmarked.
- Widest category
- Agentic work, 51.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3 0324 47.1% against GLM 4.7 Flash 98.8%, 51.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.07 against GLM 4.7 Flash $0.40 per million output tokens, 2.7x apart.
- Context window
- DeepSeek V3 0324 takes 164K tokens, GLM 4.7 Flash 200K.
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 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 1 | ||
| Math | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 2 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | Not measured | Only DeepSeek V3 0324 measured |
| AA Coding Index | 21.2 | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Vals AI) | 65.5% | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Artificial Analysis run) | 40.5% | Not measured | Only DeepSeek V3 0324 measured |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 22.0% | GLM 4.7 Flash |
| WeirdML | 36.1% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: coding | Not measured | 1382 | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | Not measured | 1389 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 14.9 | GLM 4.7 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| τ²-Bench Telecom | 47.1% | 98.8% | GLM 4.7 Flash |
| METR time horizons | 49.6% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | Not measured | Only DeepSeek V3 0324 measured |
| GPQA Diamond (Artificial Analysis run) | 65.5% | 58.1% | DeepSeek V3 0324 |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 7.6% | GLM 4.7 Flash |
| GPQA Diamond (Epoch AI run) | 67.6% | 60.5% | DeepSeek V3 0324 |
| SimpleBench | 27.2% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: hard prompts | Not measured | 1356 | Only GLM 4.7 Flash measured |
| LMArena Text: expert | Not measured | 1357 | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1369 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| AIME | 52.2% | Not measured | Only DeepSeek V3 0324 measured |
| MATH 500 (Vals AI) | 88.6% | Not measured | Only DeepSeek V3 0324 measured |
| MGSM (Vals AI) | 91.7% | Not measured | Only DeepSeek V3 0324 measured |
| AA Math Index | 41.0 | Not measured | Only DeepSeek V3 0324 measured |
| AIME 2025 (Artificial Analysis run) | 41.0% | Not measured | Only DeepSeek V3 0324 measured |
| OTIS Mock AIME | 37.8% | 58.3% | GLM 4.7 Flash |
| MATH level 5 | 75.5% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: math | Not measured | 1355 | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | Not measured | 1369 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | Not measured | Only DeepSeek V3 0324 measured |
| LegalBench (Vals AI) | 77.7% | Not measured | Only DeepSeek V3 0324 measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only DeepSeek V3 0324 measured |
| TaxEval (Vals AI) | 71.1% | Not measured | Only DeepSeek V3 0324 measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only DeepSeek V3 0324 measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: science | Not measured | 1358 | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1343 | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | Not measured | 1357 | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | Not measured | 1343 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1194 | Only GLM 4.7 Flash measured |
| Design Arena: UI components | Not measured | 1208 | Only GLM 4.7 Flash measured |
| Design Arena: game dev | Not measured | 1135 | Only GLM 4.7 Flash measured |
| Design Arena: data viz | Not measured | 1123 | Only GLM 4.7 Flash measured |
| Design Arena: 3D | Not measured | 1121 | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | Not measured | 1178 | Only GLM 4.7 Flash measured |
| Design Arena: SVG | Not measured | 1038 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 0324 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| IFBench | 41.0% | 60.8% | GLM 4.7 Flash |
| Long context reasoning (AA-LCR) | 40.7% | 41.7% | GLM 4.7 Flash |
| 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 |
| LMArena Text | Not measured | 1351 | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | Not measured | 1327 | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | Not measured | 1296 | Only GLM 4.7 Flash measured |
| LMArena Text: English | Not measured | 1373 | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | Not measured | 1330 | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | Not measured | 1403 | Only GLM 4.7 Flash measured |
| LMArena Text: German | Not measured | 1336 | Only GLM 4.7 Flash measured |
| LMArena Text: Korean | Not measured | 1283 | Only GLM 4.7 Flash measured |
| LMArena Text: French | Not measured | 1332 | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | Not measured | 1350 | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | Not measured | 1333 | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | Not measured | 1291 | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | Not measured | 1342 | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | Not measured | 1345 | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | Not measured | 1320 | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1304 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 0324 | GLM 4.7 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 4.7 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 V3 0324 | Z.ai GLM 4.7 Flash |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | March 24, 2025 | January 19, 2026 |
| Context window | 164K | 200K |
| Input $/Mtok | $0.27 | $0.0653 |
| Output $/Mtok | $1.07 | $0.40 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 0324 better than GLM 4.7 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3 0324 for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3 0324 or GLM 4.7 Flash?
DeepSeek V3 0324 costs $0.27 per million input tokens and $1.07 per million output tokens; GLM 4.7 Flash costs $0.0653 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3 0324 or GLM 4.7 Flash?
GLM 4.7 Flash, with 200K tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs GLM 4.7 Flash comparison based on?
9 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3 0324's coding index rests on 0 independent benchmarks and GLM 4.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.