Compare
Gemma 3 4B (free) vs Kimi K2.6 (free)
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
Kimi K2.6 (free)
Context window
262K
262,144 tokens · ~197K 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.
Kimi K2.6 (free) has about 8× the context window of the other in this pair.
Kimi K2.6 (free) has 700% more context capacity (262K vs 32K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Kimi K2.6 (free). Its 262K context fits entire documents without chunking (vs 32K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Gemma 3 4B (free) | Kimi K2.6 (free) |
|---|---|---|
| Context window | 32,768 tokens (32K) | 262,144 tokens (262K) |
| Max output tokens | 8,192 tokens (8K) | N/A |
| Speed tier | Balanced | Balanced |
| Vision | Yes | Yes |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | Mar 2025 | Apr 2026 |
Frequently asked questions
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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