Which AI model writes code best in October 2026? In short, Claude Sonnet 5.5: it is first on Terminal-Bench 4.0, the toughest open test of agentic coding, and paired with Claude Code it ranks first among 42 agents. Below is a ranking of models by Artificial Analysis coding benchmarks as of October 20, what each model costs on Kumo, the strongest agent-and-model pairs, and a guide to which model to pick for a given task and editor.
Terminal-Bench 4.0 leader
Sonnet 5.5
63.6% of tasks solved; Opus 5.5 gets 59.6%
Best agent
68.4
Claude Code with Sonnet 5.5 — first of 42 pairs
Coding Index leader
81.6
Claude Fable 5.1; the newest models have no index yet
Best balance
$1.1 / $5.5
Sonnet 5.5, Gemini 4 Argon and GPT-6.1 Sol per 1M tokens, input / output
The short answer: which AI writes code best
For most coding work the best choice right now is Claude Sonnet 5.5. It solves 63.6% of Terminal-Bench 4.0 tasks — more than any other model in the Kumo catalogue — and costs $1.1 / $5.5 per million input and output tokens. Claude Opus 5.5 is pricier but stronger on the hardest problems: a higher composite intelligence index (57.6 against 56.0) and the best SciCode score, 66.9%.
If you work in Codex or prefer OpenAI, take GPT-6.1 Sol: 56.1% on Terminal-Bench 4.0 at the same price as Sonnet 5.5, and paired with Codex it solves a task more cheaply than any other agent in the top ten. Gemini 4 Argon — 57.1%, also $1.1 / $5.5 — is Google's strongest model for long tasks. The highest Coding Index, 81.6, belongs to Claude Fable 5.1, but its output costs 5.5 times as much as Sonnet 5.5 and it trails it by 11.6 points on Terminal-Bench 4.0.
Which benchmarks we look at
Terminal-Bench 4.0 is a set of tasks a model solves as an agent in a real terminal: build a project, fix failing tests, set up an environment, find its way around someone else's repository. The model runs commands itself, reads their output and corrects its mistakes. That is the closest match to how Claude Code, Codex and Cline work, so it is the main test of this ranking.
Coding Index is Artificial Analysis's composite index of coding tasks. One number is convenient, but it lags: for models released in late September and October — Claude Opus 5.5, Claude Sonnet 5.5, Gemini 4 Argon and GPT-6.1 Sol — the source has no index yet. So we show it as a separate chart and do not build the main ranking on it.
SciCode is scientific programming in Python: implement a numerical method, a physical model or an algorithm from a paper. It shows how carefully a model writes computational code, where one off-by-one error ruins the whole result.
LiveCodeBench is competitive programming problems collected after a model's training cutoff so it cannot remember the answers. SWE-bench is fixing real issues from open repositories. Models of this generation have no LiveCodeBench score in the Artificial Analysis data as of October 20, and instead of SWE-bench the agent ranking below uses the newer DeepSWE v1.1 and SWE-Atlas-QnA together with Terminal-Bench v4.
Coding models ranking: the top 10
The ten strongest models of the Kumo catalogue by Terminal-Bench 4.0. Alongside: SciCode, the overall intelligence index, price per million tokens, context window and the tools where the model works through Kumo.
| # | Model | Terminal-Bench 4.0 | SciCode | Intelligence Index | Kumo price | Context | Works in |
|---|---|---|---|---|---|---|---|
| 1 | Claude Sonnet 5.5 | 63.6% | 61.0% | 56.0 | $1.1 / $5.5 | 1M | Claude Code, Codex, Cursor, Cline, OpenCode |
| 2 | Claude Opus 5.5 | 59.6% | 66.9% | 57.6 | $2.2 / $11 | 1M | Claude Code, Codex, Cursor, Cline, OpenCode |
| 3 | GPT-6 Astra | 59.1% | 56.5% | 52.7 | $6 / $30 | 1M | Codex, Cursor, Cline, OpenCode |
| 4 | Gemini 4 Argon | 57.1% | 61.8% | 52.6 | $1.1 / $5.5 | 1M | Codex, Cursor, Cline, OpenCode |
| 5 | GPT-6.1 Sol | 56.1% | 54.2% | 51.8 | $1.1 / $5.5 | 1M | Codex, Cursor, Cline, OpenCode |
| 6 | Claude Fable 5.1 | 52.0% | 63.1% | 53.4 | $6 / $30 | 1M | Claude Code, Codex, Cursor, Cline, OpenCode |
| 7 | Claude Opus 5 | 49.0% | 56.4% | 50.8 | $2.6 / $12.8 | 1M | Claude Code, Codex, Cursor, Cline, OpenCode |
| 8 | GPT-6 Sol | 43.9% | 57.6% | 47.6 | $1.1 / $5.5 | 1M | Codex, Cursor, Cline, OpenCode |
| 9 | Claude Fable 5 | 42.4% | 61.0% | 49.6 | $5.1 / $25.5 | 1M | Claude Code, Codex, Cursor, Cline, OpenCode |
| 10 | GLM-5.3 | 41.9% | 59.0% | 44.8 | $0.8 / $2.6 | 1M | Codex, Cursor, Cline, OpenCode |
The first five places go to models from three companies, with seven and a half points between first and fifth. Price matters more: three of the five — Sonnet 5.5, Gemini 4 Argon and GPT-6.1 Sol — cost the same, $1.1 / $5.5, while GPT-6 Astra, with almost the same result, costs five and a half times as much. Claude Opus 5.5 is twice the price of Sonnet 5.5 and trails it in the terminal, but is better at scientific code and long context.
The best model outside the big three is Zhipu's GLM-5.3: 41.9% on Terminal-Bench 4.0 for $0.8 / $2.6. xAI's Grok 4.7 did not make the list: it scores 25.8% on Terminal-Bench 4.0, although its predecessor Grok 4.6 holds a high Coding Index of 76.8.
Coding Index: a ranking that has not caught up with the new models
Claude Fable 5.1 leads the Coding Index with 81.6, followed by Claude Opus 5 and GPT-5.6 Sol. It is a good guide to the models of summer 2026, but it lacks the four newest flagships, which rank above Fable 5.1 on Terminal-Bench 4.0. If another ranking puts Fable 5.1 first, it is most likely built on this index.
The best agent-and-model pairs
The model is only half the result: the agent decides which files to show it, which commands to run and when to stop. Artificial Analysis separately measures 42 agent-and-model pairs on three tests — DeepSWE v1.1, SWE-Atlas-QnA and Terminal-Bench v4 — and combines them into the Coding Agent Index.
First place goes to Claude Code with Sonnet 5.5 at maximum reasoning: 68.4. But look at the cost per task: at vendor prices Claude Code with Sonnet 5.5 spends about $14.2 per task, while Codex with GPT-6.1 Sol at xhigh spends about $1 with an index of 62.9. For daily work that difference matters more than five points of index.
| Agent | Model | Index | DeepSWE v1.1 | Terminal-Bench v4 | Minutes per task | Cost per task |
|---|---|---|---|---|---|---|
| Claude Code | Claude Sonnet 5.5 | 68.4 | 72.0% | 66.2% | 87 | $14.19 |
| Devin Fusion CLI | Claude Opus 5.5 + GPT-6 Luna | 68.1 | 72.6% | 62.1% | 55 | $9.01 |
| Muse Code | Muse Wonder Full | 68.0 | 77.0% | 59.1% | 29 | no figure |
| Claude Code | Claude Opus 5.5 | 66.0 | 68.4% | 63.1% | 64 | $13.04 |
| Antigravity CLI | Gemini 4 Argon | 63.8 | 78.8% | 56.1% | 35 | $5.84 |
| Codex | GPT-6.1 Sol, xhigh | 62.9 | 73.2% | 54.5% | 16 | $1.04 |
| Claude Code | Claude Fable 5.1 | 62.2 | 64.3% | 57.6% | 35 | $12.39 |
| Codex | GPT-6 Astra | 61.6 | 67.6% | 55.6% | 29 | $7.47 |
| Opencode | GLM-5.3 | 53.6 | 61.4% | 39.9% | 48 | $4.24 |
| Kimi Code CLI | Kimi K3 | 51.9 | 68.4% | 21.2% | 61 | $5.05 |
The best model for the job
Independent data has no separate "frontend" or "backend" benchmarks, so the advice below rests on what is measured: terminal work, scientific code, long context, speed and price.
| Task | Model | Why |
|---|---|---|
| Everyday development in an agent | Claude Sonnet 5.5 | First on Terminal-Bench 4.0 (63.6%) and among agents; $1.1 / $5.5 |
| Large refactors, whole-repository work | Claude Opus 5.5 | Best SciCode (66.9%) and long-document reasoning AA-LCR at 84.7%; 1M-token context |
| Quick edits, subagents, background jobs | Claude Haiku 5.5 | 240 tokens per second and 32.8% on Terminal-Bench 4.0 for $0.06 / $0.3 |
| Frontend from screenshots and mockups | Claude Sonnet 5.5 or GLM 5.3 Flash | Both accept images; GLM 5.3 Flash scores 32.8% on Terminal-Bench 4.0 for $0.2 / $0.6 |
| Backend, infrastructure, lots of terminal work | GPT-6.1 Sol in Codex | 56.1% on Terminal-Bench 4.0 and the cheapest task among the agent leaders |
| Long tasks with documents and data | Gemini 4 Argon | 57.1% on Terminal-Bench 4.0, strong on long multi-step work; details in the Argon review |
| Scientific and numerical Python | Claude Opus 5.5 or Claude Fable 5.1 | SciCode 66.9% and 63.1%, the best in the catalogue |
| Learning and side projects | DeepSeek V4.1 Flash | $0.1 / $0.5 and 235 tokens per second; 26.8% on Terminal-Bench 4.0; details in the DeepSeek article |
Price against quality: where the best balance is
Each model on the chart is a dot: its Terminal-Bench 4.0 result up, the price of a million output tokens on Kumo across. The price axis is logarithmic: models cost from a few tens of cents to $30, and on a linear axis all the cheap ones would merge into one corner.
The best balance is the group at $5.5 output: Sonnet 5.5, Gemini 4 Argon and GPT-6.1 Sol. No model priced above them solves more tasks. Below $1 per million output tokens sit Claude Haiku 5.5, GLM 5.3 Flash and DeepSeek V4.1 Flash: they solve a quarter to a third of terminal tasks and suit simple edits, autocomplete and learning. In the middle are MiMo V2.6 Pro and GLM-5.3: clearly stronger than the cheap models and two to five times cheaper than the flagships.
Which model to use in Claude Code, Cursor, Cline and Codex
Claude Code speaks the Anthropic protocol, so through Kumo it runs Claude models. Make Sonnet 5.5 the default, switch to Opus 5.5 for the hardest tasks and give background calls to Haiku 5.5. How to install and connect Claude Code is in the Claude Code guide.
Codex uses the Responses protocol. Through Kumo it can reach GPT-6.1 Sol, GPT-6 Astra and other OpenAI models, plus models with the same protocol — Gemini 4 Argon, GLM-5.3 and Kimi K3. The best choice by cost per task is GPT-6.1 Sol.
Cursor, Cline, Roo Code, Continue and OpenCode connect as "OpenAI Compatible" and see the whole catalogue. Use Sonnet 5.5, Gemini 4 Argon or GPT-6.1 Sol there, and GLM 5.3 Flash or DeepSeek V4.1 Flash for rough work. Step-by-step setup is in the guides for Cursor, Cline and VS Code and OpenCode; a comparison of the tools themselves is in the vibe coding tools review.
Connect in five minutes
- Sign up in the Kumo console and top up your balance.
- Create an API key and set a spending limit on it right away: agents burn tokens faster than chat.
- Enter the gateway address https://api.kumorouter.com/v1 in your editor or SDK — for Claude Code, without /v1.
- Choose a model, for example claude-sonnet-5-5, gpt-6.1-sol or gemini-4-argon.
- Check the request log in the console to confirm the charge went to the right model.
One task, three models
The best way to choose is to give the same task from your own project to several models. With one Kumo key it takes three lines: only the model name changes.
from openai import OpenAI
client = OpenAI(base_url="https://api.kumorouter.com/v1", api_key="KUMO_API_KEY")
task = "Write a Python function that merges overlapping intervals, with tests."
for model in ["claude-sonnet-5-5", "gpt-6.1-sol", "gemini-4-argon"]:
answer = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": task}],
)
print(f"--- {model}\n{answer.choices[0].message.content}\n")
Frequently asked questions
- Claude or GPT — which is better for coding?
- On independent tests as of October 2026, Claude leads: Sonnet 5.5 solves 63.6% of Terminal-Bench 4.0 tasks, OpenAI's best, GPT-6 Astra, 59.1%. But GPT-6.1 Sol in the Codex agent costs several times less per task, so for a large volume of routine work it can be the better deal.
- Are there free AI models for coding?
- Vendors have free tiers, but with tight limits. On Kumo you pay per token, and the cheapest coding models — Claude Haiku 5.5 and GPT-6 Luna at $0.06 / $0.3 per million tokens — cost cents for a small project.
- Which AI writes Python best?
- For computational and scientific Python — Claude Opus 5.5: 66.9% on SciCode, the best in the catalogue, followed by Claude Fable 5.1 and Gemini 4 Argon. For everyday application code in Python the gap between leaders is small, and Sonnet 5.5 gives the best price-to-quality ratio.
- Which AI is best for vibe coding?
- For vibe coding what matters most is that the agent finishes the task on its own, so look at the agent ranking: Claude Code with Sonnet 5.5 is first. If you are just starting, read what vibe coding is and how to build a first project with AI.
- Can I use these models from Russia?
- Yes, through Kumo: the gateway works without a VPN and the balance is topped up with a Russian card, SBP or crypto. No Anthropic, OpenAI or Google account or foreign card is needed.
- How often is the ranking updated?
- Monthly and whenever notable models ship. The freshest numbers for every model are always on the benchmarks page, which updates daily.
Sources
Model scores: Artificial Analysis, snapshot of October 20, 2026. Agent ranking: Artificial Analysis Coding Agents. Prices: Kumo price list version 33 on the models page. All numbers in one place: the Kumo benchmarks page.
Read also: vibe coding for developers, new AI models from lesser-known vendors and Opus and Fable tokens at low prices.