DeepSeek-R1 for open-source llms
DeepSeek-R1 is ranked #5 on LLMDex's open-source llms ranking out of 8 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
- #5 of 8
- 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: MIT license. Beyond the task-specific fit, DeepSeek-R1 also brings open-weight reasoning model on par with o1 and cheap reasoning per token, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best open-source llms on 5 criteria , these are the axes the ranking uses, in priority order:
- Composite benchmark performance
- License permissiveness (Apache, MIT, custom OSS)
- Inference economics on commodity GPUs
- Fine-tuning ecosystem maturity
- Multilingual coverage
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, Llama 4 70B from Meta ranks one position higher and tends to win on the hardest cases. Meta's mid-tier Llama 4, the practical workhorse for self-hosted deployments.
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Other models for open-source llms
- Llama 4 405B for open-source llms
Meta's flagship open-weight model, sparse MoE design competitive with closed-frontier flagships.
Read guide - DeepSeek-V3 for open-source llms
DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.
Read guide - Qwen3-72B for open-source llms
Alibaba's flagship open-weight Qwen3, strong on multilingual, code, and math, Apache-2.0 licensed.
Read guide - Llama 4 70B for open-source llms
Meta's mid-tier Llama 4, the practical workhorse for self-hosted deployments.
Read guide - Mixtral 8×22B for open-source llms
Mistral's largest open-weight MoE, Apache-2.0, still widely deployed.
Read guide
DeepSeek-R1 for other use cases
Direct comparisons
Frequently asked
Is DeepSeek-R1 good for open-source llms?
DeepSeek-R1 is ranked #5 on LLMDex's open-source llms 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 open-source llms?
DeepSeek-R1 costs $0.55 / 1M tokens for input tokens and $2.19 / 1M tokens for output tokens. For open-source llms workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to DeepSeek-R1 for open-source llms?
The next ranked model on this task is Mixtral 8×22B. Compare both before committing.When should I NOT use DeepSeek-R1 for open-source llms?
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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