Compare
Jamba 1 5 Large vs Jp Anthropic Claude Sonnet 4 6
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Jp Anthropic Claude Sonnet 4 6
Context window
1M
1,000,000 tokens · ~750K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Jp Anthropic Claude Sonnet 4 6 has about 3.9× the context window of the other in this pair.
Jp Anthropic Claude Sonnet 4 6 has 290% more context capacity (1000K vs 256K tokens). Jamba 1 5 Large is 39% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Jp Anthropic Claude Sonnet 4 6. Its 1000K context fits entire documents without chunking (vs 256K).
RAG / high-volume retrieval
Use Jamba 1 5 Large. Input tokens are 39% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Jamba 1 5 Large. Its 256K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Jamba 1 5 Large | Jp Anthropic Claude Sonnet 4 6 |
|---|---|---|
| Context window | 256,000 tokens (256K) | 1,000,000 tokens (1000K) |
| Max output tokens | 256,000 tokens (256K) | 64,000 tokens (64K) |
| Speed tier | Deep | Balanced |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | Yes |
| Release date | N/A | N/A |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Jamba 1 5 Large in | Jamba 1 5 Large out | Jp Anthropic Claude Sonnet 4 6 in | Jp Anthropic Claude Sonnet 4 6 out |
|---|---|---|---|---|
| Ai21 | $2.00/M | $8.00/M | — | — |
| Aws Bedrock | — | — | $3.30/M | $16.50/M |
| Google Vertex | $2.00/M | $8.00/M | — | — |
| Snowflake | — | — | — | — |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model