GLM 5.3 FlashX vs GPT-5.1-Codex-Max
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.25
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 5.3 FlashX has the edge where output tokens cost 8.0x less, and where the context window is larger, 1.05M against 400K tokens.
No benchmark has tested both yet. GLM 5.3 FlashX has none yet; GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. 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
Long documents
Listed context window
GLM 5.3 FlashX
GLM 5.3 FlashX takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 FlashX
GLM 5.3 FlashX $0.0010, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- GLM 5.3 FlashX not benchmarked, GPT-5.1-Codex-Max 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-5.1-Codex-Max $10.00 per million output tokens, 8.0x apart.
- Context window
- GLM 5.3 FlashX takes 1.05M tokens, GPT-5.1-Codex-Max 400K.
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-5.1-Codex-Max | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 83.6% | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 22.2% | Only GPT-5.1-Codex-Max measured |
| Terminal-Bench | Not measured | 60.4% | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | Not measured | 1228.3 | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 34 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5.3 FlashX | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| METR time horizons | Not measured | 70.8% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 38 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5.3 FlashX | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 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-5.1-Codex-Max |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 18, 2026 | December 4, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.37 | $1.25 |
| Output $/Mtok | $1.25 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 FlashX better than GPT-5.1-Codex-Max for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 5.3 FlashX has the edge where output tokens cost 8.0x less, and where the context window is larger, 1.05M against 400K tokens.
Which is cheaper, GLM 5.3 FlashX or GPT-5.1-Codex-Max?
GLM 5.3 FlashX costs $0.37 per million input tokens and $1.25 per million output tokens; GPT-5.1-Codex-Max costs $1.25 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 5.3 FlashX or GPT-5.1-Codex-Max?
GLM 5.3 FlashX, with 1.05M tokens against 400K.
Has any benchmark tested both GLM 5.3 FlashX and GPT-5.1-Codex-Max?
No benchmark has tested both yet. GLM 5.3 FlashX has none yet; GPT-5.1-Codex-Max on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench. Compare them on price and context instead. Data as of October 21, 2026.