GLM 5.3 vs Morph V3 Fast
Morph V3 Fast costs 3.7x less per output token than GLM 5.3 ($1.20 vs $4.40 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- 70.6
- Overall
- 44.8
- Output $/M
- $4.40
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.20
GLM 5.3 or Morph V3 Fast: which to pick
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Morph V3 Fast has none, so the two cannot be ranked on coding.
Pick Morph V3 Fast for output tokens 3.7x cheaper ($1.20 vs $4.40 per million).
No benchmark has tested both yet. GLM 5.3 has results on LiveBench coding, LMArena WebDev and AA Coding Index; Morph V3 Fast has none yet. Compare them on price and context instead.
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
No call
Only GLM 5.3 has a AA Intelligence Index result, 44.8.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
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
GLM 5.3
GLM 5.3 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Morph V3 Fast
GLM 5.3 $0.0036, Morph V3 Fast $0.0014 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
- GLM 5.3 70.6 from 6 benchmarks, Morph V3 Fast not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GLM 5.3 $4.40 against Morph V3 Fast $1.20 per million output tokens, 3.7x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, Morph V3 Fast 262K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index23 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | Not measured | Only GLM 5.3 measured |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | Not measured | Only GLM 5.3 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| SciCode | 59.0% | Not measured | Only GLM 5.3 measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | Not measured | Only GLM 5.3 measured |
| LMArena Text: software and IT | 1493 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: React | 1641 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | Not measured | Only GLM 5.3 measured |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index16 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1254 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | Not measured | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index11 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | Not measured | Only GLM 5.3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | Not measured | Only GLM 5.3 measured |
| LMArena Text: expert | 1514 | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts in English | 1496 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1495 | Not measured | Only GLM 5.3 measured |
| LMArena Text: mathematical work | 1497 | Not measured | 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 measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | Not measured | Only GLM 5.3 measured |
| LMArena Text: medicine and healthcare | 1481 | Not measured | Only GLM 5.3 measured |
| LMArena Text: legal and government | 1473 | Not measured | Only GLM 5.3 measured |
| LMArena Text: business and finance | 1459 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | Not measured | Only GLM 5.3 measured |
| Design Arena: UI components | 1314 | Not measured | Only GLM 5.3 measured |
| Design Arena: game dev | 1311 | Not measured | Only GLM 5.3 measured |
| Design Arena: data viz | 1259 | Not measured | Only GLM 5.3 measured |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | Not measured | Only GLM 5.3 measured |
| Design Arena: SVG | 1293 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards measured
| Benchmark | GLM 5.3 | Morph V3 Fast | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| Long context reasoning (AA-LCR) | 79.7% | Not measured | Only GLM 5.3 measured |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| LMArena Text | 1474 | Not measured | Only GLM 5.3 measured |
| LMArena Text: instruction following | 1479 | Not measured | Only GLM 5.3 measured |
| LMArena Text: creative writing | 1458 | Not measured | Only GLM 5.3 measured |
| LMArena Text: English | 1482 | Not measured | Only GLM 5.3 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Chinese | 1532 | Not measured | Only GLM 5.3 measured |
| LMArena Text: German | 1488 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Japanese | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: French | 1503 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Spanish | 1462 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Russian | 1469 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Polish | 1466 | Not measured | Only GLM 5.3 measured |
| LMArena Text: multi-turn | 1472 | Not measured | Only GLM 5.3 measured |
| LMArena Text: longer queries | 1483 | Not measured | Only GLM 5.3 measured |
| LMArena Text: writing and language | 1469 | Not measured | Only GLM 5.3 measured |
| LMArena Text: entertainment and media | 1447 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 22 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
Morph V3 Fast costs less
Repository review
50K input + 3K output tokens
Morph V3 Fast costs less
Agent loop
200K input + 10K output tokens
Morph V3 Fast 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 | Z.ai GLM 5.3 | Morph V3 Fast |
|---|---|---|
| Maker | Z.ai | Morph |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | July 7, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.40 | $0.80 |
| Output $/Mtok | $4.40 | $1.20 |
| Coding index | 70.6 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Morph V3 Fast for coding?
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Morph V3 Fast has none, so the two cannot be ranked on coding. Pick Morph V3 Fast for output tokens 3.7x cheaper ($1.20 vs $4.40 per million).
Which is cheaper, GLM 5.3 or Morph V3 Fast?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Morph V3 Fast costs $0.80 and $1.20. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or Morph V3 Fast?
GLM 5.3, with 1.05M tokens against 262K.
Has any benchmark tested both GLM 5.3 and Morph V3 Fast?
No benchmark has tested both yet. GLM 5.3 has results on LiveBench coding, LMArena WebDev and AA Coding Index; Morph V3 Fast has none yet. Compare them on price and context instead. Data as of October 21, 2026.