Best LLM for Cline
Find the best AI model for Cline VS Code extension based on agentic coding quality, cost-effectiveness, and tool-use reliability.
Prices on this page last verified against each provider’s own pricing page
Cline is a bring-your-own-key (BYOK) agentic coding assistant for VS Code that operates through an autonomous loop: reading files, writing code, running commands, and iterating until the task is complete. Because you pay per token through your own API key, model selection is critical for both quality and cost. Unlike subscription-based tools, every token counts directly against your wallet.
The landscape has shifted a lot. Million-token context windows are now standard at the top of the market, Claude Sonnet 5 launched at the end of June and settled at $2/$10 per 1M tokens, and DeepSeek's V4 family repriced upward on 16 August 2026 and now splits its rate card into peak and off-peak hours, which lifted the floor for budget agentic coding without moving it anywhere near the frontier tiers. What makes Cline different from standard code completion tools is its agentic nature: the model needs to reliably call tools (file read, file write, terminal commands), maintain context across many iterations, and self-correct when something goes wrong. Cline supports any model through OpenRouter, direct API access, or local inference.
We ranked models by their effectiveness in real Cline workflows, considering tool-use reliability, agentic task completion rates, coding benchmarks, and cost per session. Cost is weighted more heavily here than in our other rankings because Cline users pay directly per token, making affordable models especially attractive for heavy daily usage. As always, treat rankings as a starting point; the right pick depends on your codebase and budget.
Top Models for Cline — October 2026
A dependable budget pick for real Cline work, with a 1M context that holds a working set across a long agentic loop. DeepSeek V4.1 Flash replaced V4 Flash on 10 September 2026 under the model name deepseek-flash at $0.15/$0.60 per 1M off-peak with cache hits at $0.003, and DeepSeek's own numbers put it ahead of V4-Pro on Terminal-Bench 2.1 and DeepSWE, which is why V4-Pro is being routed to it from 14 September. The figure beside this entry is the off-peak rate; traffic in the 01:00-04:00 or 06:00-10:00 UTC weekday peak windows costs exactly double, and weekends are all off-peak.
Anthropic's flagship since July 24, 2026, and the most dependable model in Cline's agentic loop: 96.0 on SWE-bench Verified and ~79.2 on SWE-bench Pro, a step up from Opus 4.8's 88.6/69.2 at the identical $5/$25. It is a premium choice for BYOK, but for gnarly multi-file refactors where a failed run costs more than the tokens it tends to pay for itself. Thinking is on by default with a low-to-max effort ladder, so budget output tokens accordingly.
The strongest quality-per-dollar pick for Cline right now. Sonnet 5 scores 85.2 on SWE-bench Verified, roughly matches Opus 4.8 on agentic evals, and offers a full 1M context with no long-context surcharge. At $2/$10 per 1M tokens it is unusually cheap for a frontier model in a BYOK tool, and that rate is now standard: Anthropic cancelled the September 1 increase to $3/$15 it had originally scheduled.
Frontier-adjacent agentic coding at roughly a third of Sonnet 5's input rate, and it is staying. On September 10 DeepSeek said every deepseek-v4-pro request would be routed to V4.1 Flash from September 14; on September 11 its pricing page reversed that, 'in response to user demand', with the billing method unchanged and no end date. DeepSeek's own numbers put the smaller, cheaper V4.1 Flash ahead of V4-Pro on most of its benchmarks, so run your evals against deepseek-flash anyway. The rate beside this entry is the off-peak one; weekday peak windows cost exactly double.
An Apache 2.0 coding specialist (80B MoE, 3B active) that scores 70.6 on SWE-bench Verified and routes through OpenRouter at the lowest input rate on this page. A popular pick in the Cline community for keeping monthly bills near zero, with the option to self-host. The 256K context is the main limitation on very large codebases.
The balanced tier of OpenAI's new GPT-5.6 family, positioned as GPT-5.5-class quality at well under half the price ($2/$12 versus $5/$30). It is now generally available and listed on OpenAI's public pricing page, so the access caveat that used to sit here no longer applies. GPT-5.5 remains the fallback if you want the older, proven model, at 2.5x the input cost.
Fast responses, a 1M context window, and $0.50/1M input make Gemini 3 Flash a strong choice for iterative Cline work where feedback speed matters more than peak reasoning. Note that thinking is on by default and thinking tokens bill as output, so real session costs run somewhat above the sticker rate.
A solid middle ground: 1M context, strong general coding ability, and a rate that undercuts the Anthropic and OpenAI flagships — with one catch the table above cannot show. Google bills this model in two bands, and a prompt over 200,000 tokens costs double on input and 1.5x on output, so the figure beside this entry holds only while your Cline context stays short. It is also still a preview model; Google ships no GA Gemini Pro. Handles Cline's tool loop well and is a sensible default for developers already in the Google ecosystem, though its agentic benchmarks trail the top two picks.
How We Ranked These Models
Order is computed, not hand-picked. Each model is scored on the published pass-rate suites it has (SWE-bench Verified 0.5, Terminal-Bench 2.1 0.5), renormalised over the suites present, then charged 5 quality points for every doubling of its blended price (3:1 input-to-output tokens). Models with no published score are never given one: they are listed after every scored model, ordered by price, and labelled as unscored. Cline is bring-your-own-key, so every token is billed to the reader and price presses harder here than on any other page: every halving of the blended price is worth 5 points of pass rate. Its loop is half file editing and half shell, so the two suites are weighted equally, and a 200K context floor keeps out models that cannot hold a working set. See the full ranking method.
Frequently Asked Questions
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The same models, weighted for another tool
Each page states its own weighting, so the order changes with the workload.