Pick for Coding autocomplete: DeepSeek V4.1 Flash at $8.99 per month. Best cited coding score for a short, frequent completion: Terminal-Bench 85.8 and DeepSWE 68.7 (huggingface.co). Prices from Models.dev. Data last refreshed Oct 16, 2026, 07:15 AM UTC.
Job
Coding autocomplete
Which model should I use for fast coding autocomplete, and what will it cost each month?
The score uses the same cited software-engineering ladder as coding agents, then subtracts a log of the monthly cost. A short completion happens often, so a cheaper cited coder rises. Cards are chosen only from models with a cited coding score. The premium card drops the price term.
How this pick is chosen
- Outputs text. Embeddings and image-only models are not in the catalog.
- Lab is one of: openai, anthropic, google, meta, mistral, deepseek, xai, alibaba, moonshotai, zhipuai, cohere, amazon, nvidia, microsoft, ai21.
- Listed by at least 5 providers. A model on only the lab and a reseller does not clear this.
- Not deprecated, and not an alpha release. Beta releases stay in.
- Not a specialized model: OCR, embeddings, rerankers, speech, safeguard, or moderation, matched on the name.
- Context window of at least 32,000 tokens.
- Has a numeric input and output price.
- Reference price is not $0. A zero in Models.dev is often an unpublished price.
- A benchmark counts only when its source is the model's lab (including that lab's Hugging Face org) or the benchmark owner. Artificial Analysis, OpenRouter, and LLM Stats are dropped. When one benchmark has several cited scores, the median is used.
- Fast-coding score: the same cited software-engineering ladder as coding agents, minus 10 times the log of the monthly cost. A short completion is most of the bill, so a cheaper cited coder rises. A model with no cited coding benchmark loses 35 before the bill. The cards are chosen only from models with a cited coding score. The premium card uses the coding ladder without the price term.
- The top pick is the highest job score. On coding agents, models within 4 points of that leader are treated as a flagship tie: the lower monthly bill is the top pick and the higher bill is the premium pick. Otherwise the premium pick is the best upgrade that costs more than the top pick. The budget pick is the highest job score that costs less than the top pick and sits at or below the 25th cost percentile. Output rates above $50 per 1M tokens stay out of the premium card. Summarization uses raw quality, not quality per dollar, when it chooses that upgrade. Remaining ties break to more providers, then a lower monthly cost, then a newer release date.
1,500 short completions a day. Each one reads about 1,200 tokens of nearby code and writes about 180. Half of the input is treated as a cached prefix.
214 models cleared the bar.304 failed the lab, provider, or specialty bar.3 failed a capability filter.1 listed at $0 were left out.0 had no usable price.
Top pick
$8.99 / month
Best cited coding score for a short, frequent completion: Terminal-Bench 85.8 and DeepSWE 68.7 (huggingface.co).
Score 44.9 · $0.15 input · $0.60 output per 1M · context 1,000,000 · 67 providers
Budget pick
$8.91 / month
Lower bill with a cited coding result: Terminal-Bench 84.3 and DeepSWE 63.4 (huggingface.co).
Score 39.1 · $0.15 input · $0.50 output per 1M · context 1,000,000 · 74 providers
Premium pick
$702 / month
Highest cited coding ladder score if you spend more: SWE-Bench Pro 80.3 and Terminal-Bench 88 (anthropic.com).
Score 9.6 · $10.00 input · $50.00 output per 1M · context 1,000,000 · 43 providers
When not to use it
Skip this page for a multi-step coding agent that edits a repo. That job is coding agents, and it does not reward the cheaper bill the same way. A cited SWE-Bench score is not a measurement of keystroke latency.
The reference workload is 1,500 completions a day, about 1,200 tokens in and 180 out, with a 50% cache hit. The cards reprice when you change those inputs.
The three cards follow the inputs. Fit, and who holds each role, stays on the reference workload.
Everyone who cleared the bar, best fit first