GLM 5.3 FlashX vs GPT-4o (2024-05-13)
GLM 5.3 FlashX costs 12.0x less per output token than GPT-4o (2024-05-13) ($1.25 vs $15.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.25
- Coding
- No data
- Overall
- 27.1
- Output $/M
- $15.00
GLM 5.3 FlashX or GPT-4o (2024-05-13): which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5.3 FlashX for output tokens 12.0x cheaper ($1.25 vs $15.00 per million) and a larger context window (1.05M against 128K tokens).
No benchmark has tested both yet. GLM 5.3 FlashX has none yet; GPT-4o (2024-05-13) on AA Coding Index, LiveCodeBench (Artificial Analysis run) and LMArena Text: coding. 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 (2024-05-13) has a AA Intelligence Index result, 7.3.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
GLM 5.3 FlashX
GLM 5.3 FlashX takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 FlashX
GLM 5.3 FlashX $0.0010, GPT-4o (2024-05-13) $0.013 per turn.
List prices
At a glance
- Coding index
- GLM 5.3 FlashX not benchmarked, GPT-4o (2024-05-13) 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 FlashX $1.25 against GPT-4o (2024-05-13) $15.00 per million output tokens, 12.0x apart.
- Context window
- GLM 5.3 FlashX takes 1.05M tokens, GPT-4o (2024-05-13) 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 Index4 boards measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 24.2 | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 33.4% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: coding | Not measured | 1297 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: software and IT | Not measured | 1297 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.3 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| METR time horizons | Not measured | 40.8% | Only GPT-4o (2024-05-13) measured |
| BALROG | Not measured | 32.3% | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 50.2% | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 52.6% | Only GPT-4o (2024-05-13) measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 1.8% | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.9% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | Not measured | 1281 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: expert | Not measured | 1250 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts in English | Not measured | 1293 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 6.3% | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | Not measured | 51.0% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | Not measured | 1284 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: mathematical work | Not measured | 1284 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 74.0% | Only GPT-4o (2024-05-13) measured |
| MMLU | Not measured | 84.2% | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | Not measured | 1295 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: medicine and healthcare | Not measured | 1278 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: legal and government | Not measured | 1314 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: business and finance | Not measured | 1262 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards measured
| Benchmark | GLM 5.3 FlashX | GPT-4o (2024-05-13) | Leader |
|---|---|---|---|
| LMArena Text | Not measured | 1300 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: instruction following | Not measured | 1278 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: creative writing | Not measured | 1291 | Only GPT-4o (2024-05-13) measured |
| LMArena Vision | Not measured | 1137 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | Not measured | 1312 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: non-English | Not measured | 1283 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Chinese | Not measured | 1277 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: German | Not measured | 1283 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Japanese | Not measured | 1257 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Korean | Not measured | 1234 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: French | Not measured | 1304 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Spanish | Not measured | 1292 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Russian | Not measured | 1285 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Polish | Not measured | 1365 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | Not measured | 1302 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: longer queries | Not measured | 1288 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: writing and language | Not measured | 1298 | Only GPT-4o (2024-05-13) measured |
| LMArena Text: entertainment and media | Not measured | 1289 | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: English | Not measured | 1147 | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | Not measured | 1132 | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 28 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 FlashX costs less
Repository review
50K input + 3K output tokens
GLM 5.3 FlashX costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 FlashX 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 FlashX | OpenAI GPT-4o (2024-05-13) |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 18, 2026 | May 13, 2024 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $0.37 | $5.00 |
| Output $/Mtok | $1.25 | $15.00 |
| 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 FlashX better than GPT-4o (2024-05-13) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5.3 FlashX for output tokens 12.0x cheaper ($1.25 vs $15.00 per million) and a larger context window (1.05M against 128K tokens).
Which is cheaper, GLM 5.3 FlashX or GPT-4o (2024-05-13)?
GLM 5.3 FlashX costs $0.37 per million input tokens and $1.25 per million output tokens; GPT-4o (2024-05-13) costs $5.00 and $15.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 5.3 FlashX or GPT-4o (2024-05-13)?
GLM 5.3 FlashX, with 1.05M tokens against 128K.
Has any benchmark tested both GLM 5.3 FlashX and GPT-4o (2024-05-13)?
No benchmark has tested both yet. GLM 5.3 FlashX has none yet; GPT-4o (2024-05-13) on AA Coding Index, LiveCodeBench (Artificial Analysis run) and LMArena Text: coding. Compare them on price and context instead. Data as of October 22, 2026.