Context Window

Compare

Jamba Mini 1 7 vs Phind Code Llama 34b Python

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.

Ai21

Model

Jamba Mini 1 7

Context window

256K

256,000 tokens · ~192K words

Model page
Meta

Model

Phind Code Llama 34b Python

Context window

16K

16,384 tokens · ~12K words

Model page

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.

Jamba Mini 1 7256K
Phind Code Llama 34b Python16K

Jamba Mini 1 7 has about 15.6× the context window of the other in this pair.

Token calculator →Memory savings →

Jamba Mini 1 7 has 1462% more context capacity (256K vs 16K tokens). Jamba Mini 1 7 is 77% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Jamba Mini 1 7. Its 256K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Jamba Mini 1 7. Input tokens are 77% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Jamba Mini 1 7. 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.

SpecJamba Mini 1 7Phind Code Llama 34b Python
Context window256,000 tokens (256K)16,384 tokens (16K)
Max output tokens256,000 tokens (256K)16,384 tokens (16K)
Speed tierFastBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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.

ProviderJamba Mini 1 7 inJamba Mini 1 7 outPhind Code Llama 34b Python inPhind Code Llama 34b Python out
Ai21$0.200/M$0.400/M——
Fireworks——$0.900/M$0.900/M

Frequently asked questions

Jamba Mini 1 7 has a larger context window: 256K tokens vs 16K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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.

Calculate your savingsLearn about Mem0

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model