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Deepseek V3p1 Terminus vs Openai Gpt Oss Safeguard 20b
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
Deepseek V3p1 Terminus
Context window
128K
128,000 tokens · ~96K 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.
Same context window size for both models.
Deepseek V3p1 Terminus and Openai Gpt Oss Safeguard 20b have identical context windows (128K tokens). Openai Gpt Oss Safeguard 20b is 87% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Openai Gpt Oss Safeguard 20b. Input tokens are 87% 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 | Deepseek V3p1 Terminus | Openai Gpt Oss Safeguard 20b |
|---|---|---|
| Context window | 128,000 tokens (128K) | 128,000 tokens (128K) |
| Max output tokens | 8,192 tokens (8K) | 8,192 tokens (8K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | Yes | No |
| Prompt caching | No | No |
| Batch API | No | No |
| 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 | Deepseek V3p1 Terminus in | Deepseek V3p1 Terminus out | Openai Gpt Oss Safeguard 20b in | Openai Gpt Oss Safeguard 20b out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $0.070/M | $0.200/M |
| Fireworks | $0.560/M | $1.68/M | — | — |
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