GPT-5.1-Codex-Max vs Saba
Saba costs 16.7x less per output token than GPT-5.1-Codex-Max ($0.60 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.60
GPT-5.1-Codex-Max or Saba: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 33K tokens).
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Saba on AA Intelligence Index, GPQA Diamond (Artificial Analysis run) and Humanity's Last Exam (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 Saba has a AA Intelligence Index result, 6.5.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
GPT-5.1-Codex-Max $0.0063, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Saba 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
- GPT-5.1-Codex-Max $10.00 against Saba $0.60 per million output tokens, 16.7x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Saba 33K.
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 | GPT-5.1-Codex-Max | Saba | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | Not measured | Only GPT-5.1-Codex-Max measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.5 | Only Saba 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 | GPT-5.1-Codex-Max | Saba | Leader |
|---|---|---|---|
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Max | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 42.4% | Only Saba measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.3% | Only Saba measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Saba | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 61.1% | Only Saba measured |
| Neither model has a result on the other 17 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
Saba costs less
Repository review
50K input + 3K output tokens
Saba costs less
Agent loop
200K input + 10K output tokens
Saba 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 | OpenAI GPT-5.1-Codex-Max | Mistral Saba |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | December 4, 2025 | February 17, 2025 |
| Context window | 400K | 33K |
| Input $/Mtok | $1.25 | $0.20 |
| Output $/Mtok | $10.00 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 33K tokens).
Which is cheaper, GPT-5.1-Codex-Max or Saba?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Saba costs $0.20 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Saba?
GPT-5.1-Codex-Max, with 400K tokens against 33K.
Has any benchmark tested both GPT-5.1-Codex-Max and Saba?
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Saba on AA Intelligence Index, GPQA Diamond (Artificial Analysis run) and Humanity's Last Exam (Artificial Analysis run). Compare them on price and context instead. Data as of October 21, 2026.