Choose AI21: Jamba Large 1.7 if…
- you need structured output (JSON schema) — to return JSON that validates against your schema; Pareto 26.10 does not advertise it
AI21: Jamba Large 1.7 vs Pareto 26.10
Neither wins outright: AI21: Jamba Large 1.7 is the pick when you need structured output (JSON schema), Pareto 26.10 when cost matters at scale.
Key numbers
| Spec | AI21: Jamba Large 1.7 | Pareto 26.10 |
|---|---|---|
| Provider | AI21 | Unbiased |
| Input price (per 1M tokens) | $2.00 | $0.80 |
| Output price (per 1M tokens) | $8.00 | $3.20 |
| Blended price (3:1 input/output) | $3.50 | $1.40 |
| Cheaper than … of all models we track | 18% | 33% |
| Context window | 256K tokens (~384 pages) | 1.0M tokens (~1,573 pages) |
| Larger context than … of all models | 45% | 80% |
| Max output per response | 4K tokens (~3,100 words) | 131K tokens (~98,300 words) |
| Input types | text | text, image |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2025-08-08 | 2026-10-15 |
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What it costs for real workloads
Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
| Workload (per month) | AI21: Jamba Large 1.7 | Pareto 26.10 | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $310 | $124 | Pareto 26.10 saves $186 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $320 | $128 | Pareto 26.10 saves $192 |
| Coding / tool-using agent 2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens) | $336 | $134 | Pareto 26.10 saves $202 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $208 | $83.20 | Pareto 26.10 saves $125 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $108 | $43.20 | Pareto 26.10 saves $64.80 |
Capabilities side by side
| Capability | AI21: Jamba Large 1.7 | Pareto 26.10 | What it lets you do |
|---|---|---|---|
| Tool / function calling | ✓ Yes | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | — | return JSON that validates against your schema |
| Reasoning / thinking mode | — | — | spend extra tokens thinking before answering hard problems |
| Image input | — | ✓ Yes | read screenshots, charts and scanned pages |
| Audio input | — | — | take speech or audio directly |
| File / PDF input | — | — | accept documents without your own parsing step |
| Image output | — | — | generate images, not just text |
| Built-in web search | — | — | answer from live web results |
| Seed (reproducible sampling) | — | — | repeat a generation for tests and evals |
| Log-probabilities | — | — | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Price history
- AI21: Jamba Large 1.7: price unchanged since we started tracking it on 2026-03-10.
- Pareto 26.10: price unchanged since we started tracking it on 2026-10-18.
Which should you choose?
Choose Pareto 26.10 if…
- cost matters at scale — it is 60% cheaper on a typical 3:1 input/output mix ($124 vs $310 a month for 50,000 support chats)
- you need to fit long inputs in one request — 1.0M tokens is roughly 1,573 pages, against 384
- you need image input — to read screenshots, charts and scanned pages; AI21: Jamba Large 1.7 does not advertise it
- you generate long outputs in one go — up to 131K tokens (about 98,300 words) per response
- you want the newer model — released 2026-10-15, 14 months after the other
Frequently asked questions
Is AI21: Jamba Large 1.7 better than Pareto 26.10?
Neither wins outright: AI21: Jamba Large 1.7 is the pick when you need structured output (JSON schema), Pareto 26.10 when cost matters at scale. In short: AI21: Jamba Large 1.7 when you need structured output (JSON schema); Pareto 26.10 when cost matters at scale, you need to fit long inputs in one request, you need image input, you generate long outputs in one go, you want the newer model.
Which is cheaper, AI21: Jamba Large 1.7 or Pareto 26.10?
Pareto 26.10 is cheaper: $1.40 vs $3.50 per 1M tokens at a typical 3:1 input/output mix, 60% less. For 50,000 support conversations a month that is $124 against $310.
How much do AI21: Jamba Large 1.7 and Pareto 26.10 cost per million tokens?
AI21: Jamba Large 1.7 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens; Pareto 26.10 costs $0.80 input and $3.20 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, AI21: Jamba Large 1.7 or Pareto 26.10?
Pareto 26.10: 1.0M tokens, about 1,573 pages, against 256K (about 384 pages) for AI21: Jamba Large 1.7.
Do AI21: Jamba Large 1.7 and Pareto 26.10 support function calling and JSON output?
Tool calling — AI21: Jamba Large 1.7: yes, Pareto 26.10: yes. JSON-schema structured output — AI21: Jamba Large 1.7: yes, Pareto 26.10: not advertised.
Can AI21: Jamba Large 1.7 or Pareto 26.10 read images?
Only Pareto 26.10 accepts image input; AI21: Jamba Large 1.7 is text-only for input.
Is AI21: Jamba Large 1.7 or Pareto 26.10 free to use?
No free variant is listed for either; both are pay-per-token.
What is the maximum output length of AI21: Jamba Large 1.7 and Pareto 26.10?
AI21: Jamba Large 1.7 can write up to 4K tokens (about 3,100 words) per response; Pareto 26.10 up to 131K (about 98,300 words).
When were AI21: Jamba Large 1.7 and Pareto 26.10 released?
AI21: Jamba Large 1.7 was listed on 2025-08-08; Pareto 26.10 on 2026-10-15.
About the two models
AI21: Jamba Large 1.7
Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context...
Full AI21: Jamba Large 1.7 specs →Pareto 26.10
Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks.
Full Pareto 26.10 specs →