AI21: Jamba Large 1.7 vs Meta: Muse Spark Open

Meta: Muse Spark Open comes out ahead on the data we track: 10 advantages and none for AI21: Jamba Large 1.7.

Key numbers

SpecAI21: Jamba Large 1.7Meta: Muse Spark Open
ProviderAI21Meta
Input price (per 1M tokens)$2.00$1.25
Output price (per 1M tokens)$8.00$4.25
Blended price (3:1 input/output)$3.50$2.00
Cheaper than … of all models we track18%25%
Context window256K tokens (~384 pages)1.0M tokens (~1,573 pages)
Larger context than … of all models45%81%
Max output per response4K tokens (~3,100 words)944K tokens (~707,800 words)
Input typestexttext, image, video, file, audio
Output typestexttext
Free variantNoNo
Listed since2025-08-082026-10-15

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 1.7Meta: Muse Spark OpenDifference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$310$179Meta: Muse Spark Open saves $131
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$320$193Meta: Muse Spark Open saves $128
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$201Meta: Muse Spark Open saves $135
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$208$126Meta: Muse Spark Open saves $82.50
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$108$66.75Meta: Muse Spark Open saves $41.25

Capabilities side by side

CapabilityAI21: Jamba Large 1.7Meta: Muse Spark OpenWhat it lets you do
Tool / function calling✓ Yes✓ Yescall your APIs and run agent loops
Structured output (JSON schema)✓ Yes✓ Yesreturn JSON that validates against your schema
Reasoning / thinking mode—✓ Yesspend extra tokens thinking before answering hard problems
Image input—✓ Yesread screenshots, charts and scanned pages
Audio input—✓ Yestake speech or audio directly
File / PDF input—✓ Yesaccept 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)—✓ Yesrepeat a generation for tests and evals
Log-probabilities—✓ Yesread token confidence for classification and scoring

From each model's published parameters and input/output types. “—” means not advertised, not proven absent.

Price history

Which should you choose?

Choose AI21: Jamba Large 1.7 if…

No advantage on the data we track — pick it for provider preference or output quality on your own prompts.

Choose Meta: Muse Spark Open if…

  • cost matters at scale — it is 43% cheaper on a typical 3:1 input/output mix ($179 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 reasoning / thinking mode — to spend extra tokens thinking before answering hard problems; AI21: Jamba Large 1.7 does not advertise it
  • you need image input — to read screenshots, charts and scanned pages; AI21: Jamba Large 1.7 does not advertise it
  • you need audio input — to take speech or audio directly; AI21: Jamba Large 1.7 does not advertise it
  • you need file / PDF input — to accept documents without your own parsing step; AI21: Jamba Large 1.7 does not advertise it
  • you need seed (reproducible sampling) — to repeat a generation for tests and evals; AI21: Jamba Large 1.7 does not advertise it
  • you need log-probabilities — to read token confidence for classification and scoring; AI21: Jamba Large 1.7 does not advertise it
  • you generate long outputs in one go — up to 944K tokens (about 707,800 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 Meta: Muse Spark Open?

Meta: Muse Spark Open comes out ahead on the data we track: 10 advantages and none for AI21: Jamba Large 1.7.

Which is cheaper, AI21: Jamba Large 1.7 or Meta: Muse Spark Open?

Meta: Muse Spark Open is cheaper: $2.00 vs $3.50 per 1M tokens at a typical 3:1 input/output mix, 43% less. For 50,000 support conversations a month that is $179 against $310.

How much do AI21: Jamba Large 1.7 and Meta: Muse Spark Open cost per million tokens?

AI21: Jamba Large 1.7 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens; Meta: Muse Spark Open costs $1.25 input and $4.25 output. Output tokens are billed separately, so long answers shift the gap.

Which has the bigger context window, AI21: Jamba Large 1.7 or Meta: Muse Spark Open?

Meta: Muse Spark Open: 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 Meta: Muse Spark Open support function calling and JSON output?

Tool calling — AI21: Jamba Large 1.7: yes, Meta: Muse Spark Open: yes. JSON-schema structured output — AI21: Jamba Large 1.7: yes, Meta: Muse Spark Open: yes.

Can AI21: Jamba Large 1.7 or Meta: Muse Spark Open read images?

Only Meta: Muse Spark Open accepts image input; AI21: Jamba Large 1.7 is text-only for input.

Is AI21: Jamba Large 1.7 or Meta: Muse Spark Open 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 Meta: Muse Spark Open?

AI21: Jamba Large 1.7 can write up to 4K tokens (about 3,100 words) per response; Meta: Muse Spark Open up to 944K (about 707,800 words).

When were AI21: Jamba Large 1.7 and Meta: Muse Spark Open released?

AI21: Jamba Large 1.7 was listed on 2025-08-08; Meta: Muse Spark Open on 2026-10-15.

About the two models

Meta

Meta: Muse Spark Open

Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers...

Full Meta: Muse Spark Open specs →

Compare AI21: Jamba Large 1.7 with other models

Compare Meta: Muse Spark Open with other models

Advertisement