DeepSeek-V3 for code completion
DeepSeek-V3 is the #2 pick on LLMDex's llm for code completion ranking out of 6 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
- #2 of 6
- Context
- 128K tokens
- Output / 1M
- $1.10 / 1M tokens
- Released
- Dec 2024
Why DeepSeek-V3 fits this task
Three things about DeepSeek-V3 that map directly onto what this task rewards: Frontier-level quality at open-weight prices; MIT license, clean commercial use; Cheap to serve via MoE architecture. Beyond the task-specific fit, DeepSeek-V3 also brings frontier-level quality at open-weight prices and mit license, clean commercial use, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best llm for code completion on 5 criteria , these are the axes the ranking uses, in priority order:
- P99 latency under 250ms for inline suggestions
- Fill-in-the-middle (FIM) capability
- Accuracy on next-token completion in mid-function context
- Cost per million tokens for high-volume editor traffic
- On-prem / self-host availability for IP-sensitive teams
How DeepSeek-V3 scores on each axis
Where DeepSeek-V3 costs you: no native vision support. 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
- Frontier-level quality at open-weight prices
- MIT license, clean commercial use
- Cheap to serve via MoE architecture
- Strong code and math
Tracked weaknesses
- No native vision support
- Geopolitical concerns for some enterprise customers
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Qwen2.5-Coder-32B from Alibaba ranks one position higher and tends to win on the hardest cases. Open-weight code specialist, frequently the top open option for self-hosted code completion.
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Other models for code completion
- Qwen2.5-Coder-32B for code completion
Open-weight code specialist, frequently the top open option for self-hosted code completion.
Read guide - Claude Haiku 4 for code completion
Anthropic's smallest 4-tier model, fast and cheap with the family's signature tone.
Read guide - GPT-5 mini for code completion
GPT-5's mid-tier sibling, most of the quality at a fraction of the price, ideal for high-volume production workloads.
Read guide - Codestral 2 for code completion
Mistral's code-specialized model, fast inline completion and strong fill-in-the-middle support.
Read guide - GPT-5 nano for code completion
OpenAI's smallest GPT-5 variant, built for ultra-low-cost classification, routing, and high-volume inference.
Read guide
DeepSeek-V3 for other use cases
Direct comparisons
Frequently asked
Is DeepSeek-V3 good for code completion?
DeepSeek-V3 is ranked #2 on LLMDex's code completion list. DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.How much does DeepSeek-V3 cost for code completion?
DeepSeek-V3 costs $0.27 / 1M tokens for input tokens and $1.10 / 1M tokens for output tokens. For code completion workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to DeepSeek-V3 for code completion?
The next ranked model on this task is Claude Haiku 4. Compare both before committing.When should I NOT use DeepSeek-V3 for code completion?
Tracked weakness: No native vision support. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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