GLM 5.3 Prime vs GPT-4o (2024-08-06)
GLM 5.3 Prime and GPT-4o (2024-08-06) cost about the same per output token ($8.80 vs $10.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $8.80
- Coding
- No data
- Overall
- 26.3
- Output $/M
- $10.00
GLM 5.3 Prime or GPT-4o (2024-08-06): which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5.3 Prime for output tokens 1.1x cheaper ($8.80 vs $10.00 per million) and a larger context window (1M against 128K tokens).
No benchmark has tested both yet. GLM 5.3 Prime has none yet; GPT-4o (2024-08-06) on Aider polyglot, SWE-bench Lite and LiveCodeBench (Artificial Analysis run). 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-4o (2024-08-06) has a AA Intelligence Index result, 7.7.
Directional only
Math
AIME
No call
Only GPT-4o (2024-08-06) has a AIME result, 14.0%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-08-06) has a MMLU Pro result, 74.1%.
Directional only
Long documents
Listed context window
GLM 5.3 Prime
GLM 5.3 Prime takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Prime
GLM 5.3 Prime $0.0072, GPT-4o (2024-08-06) $0.0075 per turn.
List prices
At a glance
- Coding index
- GLM 5.3 Prime not benchmarked, GPT-4o (2024-08-06) 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 Prime $8.80 against GPT-4o (2024-08-06) $10.00 per million output tokens, 1.1x apart.
- Context window
- GLM 5.3 Prime takes 1M tokens, GPT-4o (2024-08-06) 128K.
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 Index6 boards measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 23.1% | Only GPT-4o (2024-08-06) measured |
| SWE-bench Lite | Not measured | 21.7% | Only GPT-4o (2024-08-06) measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 31.7% | Only GPT-4o (2024-08-06) measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 8.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: coding | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: software and IT | Not measured | 1278 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.7 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 28.9% | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMMU Pro (Vals AI) | Not measured | 64.0% | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 52.1% | Only GPT-4o (2024-08-06) measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 2.3% | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Epoch AI run) | Not measured | 49.2% | Only GPT-4o (2024-08-06) measured |
| SimpleBench | Not measured | 17.8% | Only GPT-4o (2024-08-06) measured |
| Chess puzzles | Not measured | 6.0% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: hard prompts | Not measured | 1264 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: expert | Not measured | 1241 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: hard prompts in English | Not measured | 1277 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| AIME | Not measured | 14.0% | Only GPT-4o (2024-08-06) measured |
| MATH 500 (Vals AI) | Not measured | 75.2% | Only GPT-4o (2024-08-06) measured |
| MGSM (Vals AI) | Not measured | 90.7% | Only GPT-4o (2024-08-06) measured |
| OTIS Mock AIME | Not measured | 6.4% | Only GPT-4o (2024-08-06) measured |
| FrontierMath, tiers 1 to 3 | Not measured | 0.4% | Only GPT-4o (2024-08-06) measured |
| MATH level 5 | Not measured | 53.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: math | Not measured | 1285 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: mathematical work | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 74.1% | Only GPT-4o (2024-08-06) measured |
| LegalBench (Vals AI) | Not measured | 80.1% | Only GPT-4o (2024-08-06) measured |
| CorpFin (Vals AI) | Not measured | 39.4% | Only GPT-4o (2024-08-06) measured |
| TaxEval (Vals AI) | Not measured | 71.1% | Only GPT-4o (2024-08-06) measured |
| MortgageTax (Vals AI) | Not measured | 61.0% | Only GPT-4o (2024-08-06) measured |
| MedQA (Vals AI) | Not measured | 88.2% | Only GPT-4o (2024-08-06) measured |
| SimpleQA Verified | Not measured | 26.0% | Only GPT-4o (2024-08-06) measured |
| MMLU | Not measured | 84.3% | Only GPT-4o (2024-08-06) measured |
| LMArena Text: science | Not measured | 1274 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: medicine and healthcare | Not measured | 1252 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: legal and government | Not measured | 1275 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: business and finance | Not measured | 1232 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards measured
| Benchmark | GLM 5.3 Prime | GPT-4o (2024-08-06) | Leader |
|---|---|---|---|
| IFBench | Not measured | 36.0% | Only GPT-4o (2024-08-06) measured |
| Long context reasoning (AA-LCR) | Not measured | 41.0% | Only GPT-4o (2024-08-06) measured |
| LMArena Text | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: instruction following | Not measured | 1267 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: creative writing | Not measured | 1274 | Only GPT-4o (2024-08-06) measured |
| LMArena Vision | Not measured | 1065 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: English | Not measured | 1297 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: non-English | Not measured | 1262 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Chinese | Not measured | 1254 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: German | Not measured | 1257 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Japanese | Not measured | 1234 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Korean | Not measured | 1218 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: French | Not measured | 1263 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Spanish | Not measured | 1269 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Russian | Not measured | 1272 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: multi-turn | Not measured | 1279 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: longer queries | Not measured | 1283 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: writing and language | Not measured | 1290 | Only GPT-4o (2024-08-06) measured |
| LMArena Text: entertainment and media | Not measured | 1267 | Only GPT-4o (2024-08-06) measured |
| LMArena Vision: English | Not measured | 1072 | Only GPT-4o (2024-08-06) measured |
| LMArena Vision: Chinese | Not measured | 1009 | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 27 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 5.3 Prime costs less
Repository review
50K input + 3K output tokens
GPT-4o (2024-08-06) costs less
Agent loop
200K input + 10K output tokens
GPT-4o (2024-08-06) 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 Prime | OpenAI GPT-4o (2024-08-06) |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 23, 2026 | August 6, 2024 |
| Context window | 1M | 128K |
| Input $/Mtok | $2.80 | $2.50 |
| Output $/Mtok | $8.80 | $10.00 |
| 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 GLM 5.3 Prime better than GPT-4o (2024-08-06) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5.3 Prime for output tokens 1.1x cheaper ($8.80 vs $10.00 per million) and a larger context window (1M against 128K tokens).
Which is cheaper, GLM 5.3 Prime or GPT-4o (2024-08-06)?
GLM 5.3 Prime costs $2.80 per million input tokens and $8.80 per million output tokens; GPT-4o (2024-08-06) costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 5.3 Prime or GPT-4o (2024-08-06)?
GLM 5.3 Prime, with 1M tokens against 128K.
Has any benchmark tested both GLM 5.3 Prime and GPT-4o (2024-08-06)?
No benchmark has tested both yet. GLM 5.3 Prime has none yet; GPT-4o (2024-08-06) on Aider polyglot, SWE-bench Lite and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead. Data as of October 22, 2026.