Model comparison
GLM-5.3 vs Jamba Large
GLM-5.3 has enough public results to be ranked (#26); Jamba Large does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in reasoning, where GLM-5.3 leads 46.1 to 18.0.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $8 for Jamba Large.
- GLM-5.3 accepts more context: 1M tokens versus 256K.
Side by side
| GLM-5.3 | Jamba Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | AI21 Labs |
| Noometry Index | 54.8 | 33.1 |
| Released | 2026-08-14 | 2025-07-01 |
| Weights | Open | Open |
| Context window | 1M | 256K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1.40 | $2 |
| Output $ / M tokens | $4.40 | $8 |
| Results tracked | 42 | 2 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
GLM-5.3: 59.5 (#14), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| LMArena Coding | 1496 | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Jamba Large: 18.0
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| Kagi LLM Benchmark | — | 26.1% |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| LMArena Hard Prompts | 1489 | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |
Math Not comparable
GLM-5.3: 62.3 (#33), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| LMArena Math | 1489 | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Jamba Large: 37.7
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 9.7% |
| LMArena Expert | 1516 | — |
Multilingual Not comparable
GLM-5.3: 55.7 (#28), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| LMArena Non-English | 1457 | — |
| LMArena Chinese | 1528 | — |
| LMArena French | 1499 | — |
| LMArena German | 1499 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1472 | — |
| LMArena Russian | 1463 | — |
| LMArena Spanish | 1460 | — |
Instruction Following Not comparable
GLM-5.3: 77.5 (#23), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| LMArena Instruction Following | 1477 | — |
Long Context Not comparable
GLM-5.3: 45.4 (#41), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference Not comparable
GLM-5.3: 75.7 (#6), Jamba Large: —
| Benchmark | GLM-5.3 | Jamba Large |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1457 | — |
| EQ-Bench Creative Writing | 2075 | — |
| LMArena Multi-Turn | 1472 | — |
Frequently asked questions
Is GLM-5.3 better than Jamba Large?
GLM-5.3 has enough public results to be ranked (#26); Jamba Large does not yet, so treat this comparison as directional.
Which is cheaper, GLM-5.3 or Jamba Large?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Jamba Large lists at $2 and $8.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 256K.
How many benchmarks do GLM-5.3 and Jamba Large share?
0 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Jamba Large has 2.