GPT-5 nano for code completion
GPT-5 nano is ranked #6 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
- #6 of 6
- Context
- 400K tokens
- Output / 1M
- $0.40 / 1M tokens
- Released
- Aug 2025
Why GPT-5 nano fits this task
Three things about GPT-5 nano that map directly onto what this task rewards: Fast P99 latency. Beyond the task-specific fit, GPT-5 nano also brings lowest-cost openai model with vision support and good enough for routing and classification, 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 GPT-5 nano scores on each axis
Where GPT-5 nano costs you: visible quality gap on open-ended tasks. 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
- Lowest-cost OpenAI model with vision support
- Fast P99 latency
- Good enough for routing and classification
Tracked weaknesses
- Visible quality gap on open-ended tasks
- Limited reasoning capability
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Codestral 2 from Mistral ranks one position higher and tends to win on the hardest cases. Mistral's code-specialized model, fast inline completion and strong fill-in-the-middle support.
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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 - DeepSeek-V3 for code completion
DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.
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 other use cases
Direct comparisons
Frequently asked
Is GPT-5 nano good for code completion?
GPT-5 nano is ranked #6 on LLMDex's code completion list. OpenAI's smallest GPT-5 variant, built for ultra-low-cost classification, routing, and high-volume inference.How much does GPT-5 nano cost for code completion?
GPT-5 nano costs $0.050 / 1M tokens for input tokens and $0.40 / 1M tokens for output tokens. For code completion workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to GPT-5 nano for code completion?
Look at the full Best LLM for Code Completion ranking for cheaper picks at lower ranks.When should I NOT use GPT-5 nano for code completion?
Tracked weakness: Visible quality gap on open-ended tasks. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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