jamba-large-1.6 vs jamba-large-1.7
jamba-large-1.6 and jamba-large-1.7 are priced within 15% of each other on input.
| jamba-large-1.6 | jamba-large-1.7 | |
|---|---|---|
| Input / 1M | $2.00 | $2.00 |
| Cached input / 1M | — | — |
| Output / 1M | $8.00 | $8.00 |
| Context window | 256K | 256K |
| Max output | 256K | 256K |
| Provider | AI21 | AI21 |
| Vision | No | No |
| Tool calling | No | No |
| Token count | estimated | estimated |
Capability for the money
Independent measurement · Artificial Analysis · read 2026-08-11jamba-large-1.6 and jamba-large-1.7 are close on price and level on every benchmark they share — either will do.
Coding
writing and fixing code
LiveCodeBenchjamba-large-1.7 +0.9
Hard reasoning
graduate-level science and multi-step analysis
GPQA Diamondjamba-large-1.7 +0.3
Averaged over the benchmarks both models sat, jamba-large-1.7 returns 8.2 points of pass rate per dollar of blended price against 8.0 — the ratio that decides whether the dearer model earns its rate.
Each figure is a pass rate: the share of that test set the model answered correctly, measured by a third party rather than self-reported by the lab. Rows are different tests of different difficulty, so compare a model against the other model, not one row against the next. No published following instructions or tool use score for one of these models, so those lenses are omitted. And a benchmark is not your workload — it narrows the shortlist, it does not pick for you.
Which to pick
- Cheaper input: jamba-large-1.6
- Cheaper output: jamba-large-1.6
- Larger context: jamba-large-1.6
- Better at coding: jamba-large-1.7 (17.2% vs 18.1% on LiveCodeBench)
- Better at hard reasoning: jamba-large-1.7 (38.7% vs 39.0% on GPQA Diamond)
List price is not production cost. Output length, cache hit rate, retries and task quality move the real number more than the headline rate does — price your own prompt on the token ledger before deciding.
Related comparisons
jamba-large-1.6 detail · jamba-large-1.7 detail · All AI21 pricing · All comparisons