GLM 4.6V vs Saba
Updated October 21, 2026. 4 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.6V has the edge where the context window is larger, 131K against 33K tokens.
- Shared results
- 4
- GLM 4.6V only
- 34
- Saba only
- 0
- Like-for-like categories
- 3 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
GLM 4.6V
GLM 4.6V leads, 11.2 against 6.5.
Same board, both measured
Long documents
Listed context window
GLM 4.6V
GLM 4.6V takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Saba
GLM 4.6V $0.0008, Saba $0.0005 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Saba not benchmarked.
- Widest category
- Knowledge, 18.8 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GLM 4.6V 71.9% against Saba 42.4%, 29.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Saba $0.60 per million output tokens, 1.5x apart.
- Context window
- GLM 4.6V takes 131K tokens, Saba 33K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 4.6V | Bars | Saba | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | Not measured | Only GLM 4.6V measured |
| LMArena Text: coding | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: software and IT | 1391 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 6.5 | GLM 4.6V |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 42.4% | GLM 4.6V |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 4.3% | GLM 4.6V |
| LMArena Text: hard prompts | 1367 | Not measured | Only GLM 4.6V measured |
| LMArena Text: expert | 1371 | Not measured | Only GLM 4.6V measured |
| LMArena Text: hard prompts in English | 1391 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | 61.1% | GLM 4.6V |
| LMArena Text: science | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: medicine and healthcare | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: legal and government | 1393 | Not measured | Only GLM 4.6V measured |
| LMArena Text: business and finance | 1359 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| IFBench | 30.1% | Not measured | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | 48.7% | Not measured | Only GLM 4.6V measured |
| LMArena Text | 1376 | Not measured | Only GLM 4.6V measured |
| LMArena Text: instruction following | 1350 | Not measured | Only GLM 4.6V measured |
| LMArena Text: creative writing | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | Not measured | Only GLM 4.6V measured |
| LMArena Text: non-English | 1358 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Chinese | 1425 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Russian | 1339 | Not measured | Only GLM 4.6V measured |
| LMArena Text: multi-turn | 1359 | Not measured | Only GLM 4.6V measured |
| LMArena Text: longer queries | 1357 | Not measured | Only GLM 4.6V measured |
| LMArena Text: writing and language | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Text: entertainment and media | 1343 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Saba | Leader |
|---|---|---|---|
| Neither model has a result on 5 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 | Z.ai GLM 4.6V | Mistral Saba |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 8, 2025 | February 17, 2025 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.30 | $0.20 |
| Output $/Mtok | $0.90 | $0.60 |
| 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 4.6V better than Saba for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.6V has the edge where the context window is larger, 131K against 33K tokens.
Which is cheaper, GLM 4.6V or Saba?
GLM 4.6V costs $0.30 per million input tokens and $0.90 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, GLM 4.6V or Saba?
GLM 4.6V, with 131K tokens against 33K.
How many benchmarks is the GLM 4.6V vs Saba comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Saba's on 0. Where only one model has a result, the page says so instead of naming a winner.