DeepSeek-R1 for red-teaming
DeepSeek-R1 is the #3 pick on LLMDex's llms for red-teaming ranking out of 4 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #3 of 4
- Context
- 128K tokens
- Output / 1M
- $2.19 / 1M tokens
- Released
- Jan 2025
Why DeepSeek-R1 fits this task
Three things about DeepSeek-R1 that map directly onto what this task rewards: Open-weight reasoning model on par with o1; Cheap reasoning per token. Beyond the task-specific fit, DeepSeek-R1 also brings mit license, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best llms for red-teaming on 5 criteria , these are the axes the ranking uses, in priority order:
- Creativity on attack vectors
- Coverage across harm categories
- Self-monitoring (don't generate truly harmful payloads)
- Reasoning depth on multi-step attacks
- Honesty in reporting back
How DeepSeek-R1 scores on each axis
Where DeepSeek-R1 costs you: slow, reasoning is slow by design. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Open-weight reasoning model on par with o1
- MIT license
- Cheap reasoning per token
Tracked weaknesses
- Slow, reasoning is slow by design
- No vision
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, GPT-5.5 from OpenAI ranks one position higher and tends to win on the hardest cases. OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.
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Other models for red-teaming
- Claude Opus 4.7 for red-teaming
Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.
Read guide - GPT-5.5 for red-teaming
OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.
Read guide - Gemini 3 Pro for red-teaming
Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.
Read guide
DeepSeek-R1 for other use cases
Direct comparisons
Frequently asked
Is DeepSeek-R1 good for red-teaming?
DeepSeek-R1 is ranked #3 on LLMDex's red-teaming list. First open-weight reasoning model to match o1, the release that proved RL-from-scratch reasoning training was reproducible.How much does DeepSeek-R1 cost for red-teaming?
DeepSeek-R1 costs $0.55 / 1M tokens for input tokens and $2.19 / 1M tokens for output tokens. For red-teaming workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to DeepSeek-R1 for red-teaming?
The next ranked model on this task is Gemini 3 Pro. Compare both before committing.When should I NOT use DeepSeek-R1 for red-teaming?
Tracked weakness: Slow, reasoning is slow by design. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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