Compare
Gpt 35 Turbo Instruct 0914 vs Kimi K2 Thinking
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.
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 Thinking has about 64× the context window of the other in this pair.
Kimi K2 Thinking has 6298% more context capacity (262K vs 4K tokens). Kimi K2 Thinking is 60% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Kimi K2 Thinking. Its 262K context fits entire documents without chunking (vs 4K).
RAG / high-volume retrieval
Use Kimi K2 Thinking. Input tokens are 60% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Gpt 35 Turbo Instruct 0914 | Kimi K2 Thinking |
|---|---|---|
| Context window | 4,097 tokens (4K) | 262,144 tokens (262K) |
| Max output tokens | N/A | 262,144 tokens (262K) |
| Speed tier | Balanced | Deep |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | N/A | Nov 2025 |
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 | Gpt 35 Turbo Instruct 0914 in | Gpt 35 Turbo Instruct 0914 out | Kimi K2 Thinking in | Kimi K2 Thinking out |
|---|---|---|---|---|
| Azure | $1.50/M | $2.00/M | — | — |
| Baseten | — | — | $0.600/M | $2.50/M |
| Fireworks | — | — | $0.600/M | $2.50/M |
| Gmi | — | — | $0.800/M | $1.20/M |
| Moonshot | — | — | $0.600/M | $2.50/M |
| Novita | — | — | $0.600/M | $2.50/M |
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