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A workshop keeps several benches. One saw is sharper. Another vise holds the work better. A third board is cheap enough that a bad cut does not ruin the week. An AI coding setup works the same way. The sharpest blade on a poster and the bench that finishes a real task by evening are rarely the same object.
Four things have to show up together before a patch is accepted. The model has to understand code, a long context, and someone else's error. The environment has to see the repository, edit several files, and show the difference. The agent loop has to walk from the task to a check, and stop short of a lone function only when that is all you asked for. The bill has to count the finished task: retries, context, the subscription, and the person's time.
The practical choice is a bundle: model, agent, IDE, cost, and a result you can verify. What follows is a snapshot for 25 October 2026. Rankings and prices move. Before you buy, check the version name, the reasoning mode, the limits, and the price on the vendor's page. The outcome depends on the test harness.
How an IDE indexes a repo and switches between suggestion, chat, and agent is covered in how AI IDEs work with code. The token bill inside one loop is in the token economics of AI coding. This piece asks a different question: which bench belongs under your work.
Key takeaways
Compare the bundle, not the logo. The same model in an editor and in a terminal agent reads the repository differently and returns a different patch.
Count money on the accepted task. Subscription share, tokens, your own hardware, human review, and rework add up to one sum. The price per million tokens does not replace it.
Scores from different tests sit on different rulers. Closing a repository issue, working in a terminal, and generating a short function measure different skills. Even one test moves when the attempt count, the allowed commands, or network access changes.
Your own set of 15–30 tasks is more honest than someone else's ranking. Same commit, same commands, same time limit. Watch the share of accepted changes, time to green tests, and cost per accepted result.
Mature company code is chosen for verifiability. On an accounting system with historical rules, a safe change to a call chain matters more than a lead on short functions.
A Cursor alternative is the whole bench
Cursor puts an editor, repository context, inline suggestion, chat, multi-file edits, and an agent mode in one place. A "replacement" is almost never a single product. Split the market into classes and say plainly which job each class takes.
Interactive editing in a familiar window, handing a whole ticket to an agent, and tight control of the model provider are three different purchases. You can combine them. You cannot drop them into one table cell: the scenario the tool was not built for is the one that loses.
Editors with a model already inside
| Tool | Strength | Closer fit | Watch |
|---|---|---|---|
| Cursor | Editor, repository context, and agent in one window | Daily work with little setup | Plan, limits on Cursor's own models, and extra cost for others |
| Windsurf | An environment built around agent workflows | People who want a similar daily motion | Current models, limits, and the price of heavy use |
| VS Code + GitHub Copilot | The editor ecosystem, GitHub, and company features | Teams already living in GitHub | Plan and mode change what you can do |
| Antigravity | Google's agent approach to tasks | People who want the agent to take more steps | Availability, limits, and supported models |
| Zed | A fast editor and collaboration | People who care about speed and a small surface | AI features and integrations are thinner than Cursor's |
| JetBrains + Junie | A deep tie to a language-specific IDE | Java, Kotlin, PHP, Python | Which agent features the plan includes |
| Replit | Browser: write, run, show | Prototypes, learning, small web products | A heavy company repository fits poorly |
Agents beside the editor
| Tool | Scenario | Gain | Tradeoff |
|---|---|---|---|
| Claude Code | A codebase from the terminal, with commands and tests | Multi-file tasks that need a real check | You control permissions and live in the terminal |
| Codex | Handing a task to an agent, including an isolated environment | Separate fixes and parallel work | You still review the result; access depends on the plan |
| Gemini CLI | A terminal agent in Google's ecosystem | Gemini models and an extensible CLI process | Quality depends on the model, the context, and the setup |
| Aider | Pair work in the terminal across models | Model choice and a visible Git trail | You configure models and the API budget |
| Continue | AI features in an IDE with a choice of provider | Flexibility, including local models | Configuration and quality sit with you |
| Cline / Roo Code | Agent actions inside VS Code | Control of files, commands, and provider | A long loop burns tokens quickly |
| OpenHands | More autonomous work on an engineering task | Experiments with an agent workflow | You need infrastructure, setup, and close supervision |
The list does not promise interchangeability. Some products are stronger while you edit live. Others are stronger when you hand off a task and check the result.
Which class fits the job
- A familiar editor with AI: Cursor, Windsurf, VS Code with GitHub Copilot, JetBrains.
- A whole ticket for an agent: Claude Code, Codex, Gemini CLI, Cline, or Roo Code.
- Your own provider and a visible bill: Aider, Continue, Cline, Roo Code.
- A write-run-show loop in the browser: Replit.
- Company code: besides generation quality, look at data retention, access, audit, proxy, and the right to use only approved models.
The version name beats the family crown
A family usually has a fast cheap option and an expensive deep-reasoning mode. Naming a single winner for the next quarter is pointless: next month the box carries a different version. A technical article should record the exact name, the check date, the reasoning mode, the context limit, and the test conditions.
| Family | Where to check | What to watch on your tasks |
|---|---|---|
| Claude, Sonnet and Opus lines | Anthropic's materials and independent coding boards | Refactors, large changes, following requirements, patch quality |
| GPT and the Codex line | OpenAI's materials, SWE-bench, and independent evaluations | Engineering tasks, tools, tests, and repairs after a run |
| Gemini | Google's materials and independent coding boards | Long context, multimodal input, speed, agent scenarios |
| DeepSeek | Official releases and independent runs | Price against quality, reasoning, API, and a host that fits |
| Qwen Coder | Qwen releases and open boards | Open weights, local runs, code, and language coverage |
| Mistral, Codestral, and narrow models | Releases and fresh runs | Speed, price, license, local runs, autocomplete |
Scores from different tests do not share a scale
Benches measure different skills. A high score on short function generation says nothing about whether an agent will close a ticket in someone else's repository.
SWE-bench Verified asks whether an agent solves selected real tasks from GitHub repositories in a given environment. SWE-bench Pro takes a harder set and tries to reduce the effect of training on public examples. LiveCodeBench splits tasks by time so a fresh model does not win on conditions it has already seen. The Aider Polyglot board edits existing files in several languages and checks the patch. Terminal-Bench gives the agent a terminal and tools on multi-step tasks. HumanEval and MBPP are useful as a check of basic function generation and say little about a large repository. Artificial Analysis and neighboring independent ratings help line up quality, speed, and cost once you have read the method.
The same test drifts when the harness, the attempt count, the tool set, the time limit, or permission to search the network changes. Two percentages from different pages cannot be subtracted into a winner.
For daily work in ERP, React, TypeScript, and PHP I watch five things. Does the model find the right places and hold the architecture. Does it edit components, API clients, types, and tests together. Does it run tests, the linter, and the build, and understand the error. Does it keep the constraints and leave neighboring code alone. Does it repair its own miss after the tests answer.
Speed, stability, context length, the price of a successful solution, data policy, and the ability to turn external connections off sit beside those five.
A subscription, an API, and your own machine bill different things
A monthly subscription looks simpler than an API invoice. Comparing only that number misleads. The subscription hides limits that change. The API bills input and output, and sometimes cache and tools separately.
An editor subscription is a predictable payment and fits one person who works every day. Before you buy, learn what happens after the included volume runs out and which models the plan contains.
A pay-as-you-go API lets you pick the model and the provider. It fits automation, experiments, and irregular work. The sum depends on token count, context length, retries, and tools. How a token is counted inside a loop is in the token economics piece.
A local model reduces dependence on an external API and gives more control over data. You need suitable hardware, setup, maintenance, and time to judge quality. Free tokens do not mean free operation: the estimate includes GPU, memory, electricity, and the team's hours.
Count the cost of a successfully closed task:
task_cost = subscription_share + API + compute + review + reworkSubscription share is the slice of the plan for the period that landed on this work. API is the actual tokens and tools. Compute is your hardware and infrastructure. Review is the person's time on the patch. Rework is the repair of a miss and of the consequences of a bad solution.
A useful fraction divides useful accepted changes by full cost. Three numbers are clearer in practice. Cost of an accepted pull request. Time to green tests. Share of tasks that passed review without a substantial rewrite.
| Approach | Quality potential | Cost predictability | Flexibility | Result |
|---|---|---|---|---|
| Cursor, Windsurf, or Copilot subscription | High with a good model | Medium: limits exist | Medium | A solid daily start |
| Claude Code, Codex, or Gemini CLI on a plan | High on agent tasks | Depends on limits and mode | Medium | Fits when you hand off the whole task |
| Aider, Continue, Cline, plus an API | Medium to very high | Medium: you see the provider, the loop eats tokens | High | A strong option for an experienced developer |
| Local open-weight models | Depends heavily on model and hardware | Steadier after purchase; infrastructure remains | Very high | Makes sense when data rules require it |
| Free quotas and light models | Enough for simple edits | High until the quota ends | Lower | Learning and small edits |
This is not a numeric ranking. The result depends on current models, prices, and how hard you use them.
Budget decides the pilot
On a minimal budget, start with free quotas and the editor you already have open. Give small edits to a fast model. Attach the expensive one to hard tasks. Do not put the whole repository into context without a reason.
On a medium budget, pick one main IDE and one agent alternative. For two to four weeks run the same real tasks and compare the subscription with the API on the facts. Cap the number of parallel agents: otherwise both the bill and conflicting edits grow.
At high intensity, or in company development, compare the team plan, access, audit, source protection, and administration. Run the pilot on real tickets. Count the cost of an accepted change and the effect on lead time, defects, and review load.
For React and TypeScript, look for a bundle that reads the project, finds types and API clients, edits several files, runs tests, and keeps the patterns you already accepted. Compare Cursor or Windsurf with VS Code and Copilot, and with one terminal agent.
For PHP and large ERP systems, a pretty new component proves little. What matters is how safely the model changes a mature system: business rules, stored procedures, integrations, historical constraints. Put these in the pilot: find a call chain, change behavior across several layers, add regression tests, write a migration plan without an immediate mass rewrite, optimize a query without changing its meaning, and open a small pull request with a short patch. On those tasks an agent with tests and change control is often worth more than a model that leads on short functions.
For open and local development, Aider, Continue, Cline, and Roo Code let you choose the provider. Judge Qwen Coder and other open-weight models by license, quality on the languages you need, size, quantization, and speed on your hardware. A local run by itself does not make a model cheaper than a closed frontier model, and it does not equalize quality.
A team needs two weeks and 20–30 representative tasks. Where you can, hide which model solved the task, and keep one review standard. Record the share of tasks without a substantial rewrite, time to the first correct solution, number of runs and repairs, regressions, cost of an accepted change, people's satisfaction, and fit with security rules. How to check agent output in the wider process sits closer to the eval harness and to agentic engineering on a team.
Your own bench beats someone else's poster
Take 15–30 real tasks from your project. Sort them into bins: finding the cause of a bug, a small feature, a refactor, tests, SQL and optimization, a migration. Freeze the same commit, the same requirements, and the same set of commands. Acceptance criteria and the time limit stay the same too. If the result jumps from run to run, repeat the task.
| Metric | How to measure |
|---|---|
| Functional success | Tasks that met the checkable criteria |
| Green checks | Solutions with passing tests and a successful build |
| Review acceptance | Changes accepted after an ordinary review |
| Regressions | How many regressions landed on a task |
| Time to an accepted solution | From start to a result you took |
| Cost per accepted task | Full spend divided by the number of accepted tasks |
| Context care | How often the agent reads extra files and repeats work |
Line count and generation speed are poor judges here. A short correct patch is usually worth more than a long confident text nobody ran. What a person should look at after an agent is collected in code review in the AI era.
What to ask before a company repository
Before an agent sees company code, check which files go to the provider and whether requests are used for training. Learn where logs and history live. Find out whether the agent can run commands and reach the network, how write and test permissions work, and whether you can exclude secrets, .env, keys, dumps, and personal data. Look separately for company administration and audit.
The working rules are plain. Real secrets stay out of requests. Permissions stay minimal. Dangerous commands stay limited. Someone reads the patch. Tests run in an isolated environment. The agent does not ship to the live environment on its own, without a separate approval. More on that boundary is in secure AI development.
A snapshot for 25 October 2026: tests and prices
Prices below are in US dollars, before tax. Before you buy, check availability in your country, currency, limits, and the offer text. These are other people's published figures on the preparation date, not a measurement from our lab.
On Terminal-Bench, where an agent performs hard multi-step tasks through a terminal, the published board is led by Opus 5.5 with Claude Code: 64.8% ± 3.1%. Next is Sonnet 5.5 with Claude Code at 61.8% ± 2.9%. Then GPT-6 Astra with Codex and GPT-6.1 Sol with Codex, each at 58.2% in the stated configuration. These are results of a "model + agent + settings" bundle. They are not a pure comparison of models in an empty room. Source: the published run board.
Anthropic reports that on FrontierCode v1.1, Opus 5.5 in medium mode scored 54.6%, and on CursorBench 4.0 it scored 52.5%. Those are the model maker's figures. Put them next to an independent run and read the method. Source: the maker's page.
The practical reading is narrow. Opus 5.5 looks like one of the strongest options for hard agent work in a terminal. GPT-6 Astra and GPT-6.1 Sol sit nearby on the same kind of task. Price and quality still depend on the cost of a run and on how the agent walks the repository. A Terminal-Bench percentage does not transfer to autocomplete or to a simple interface edit.
| Product / plan | Published price | What to account for |
|---|---|---|
| Cursor Plus | $8 / month | A base limit on Cursor's own models; third-party models are not included |
| Cursor Premium | $30 / month | More included usage, cloud agents, third-party models at API rates |
| Cursor Super | $100 / month | Heavier use |
| Cursor Ultra | $200 / month | Several agents and dense work |
| GitHub Copilot Pro | $10 / month | Daily programming; the credit volume is limited |
| GitHub Copilot Pro+ | $39 / month | More credits and access to more expensive models |
| GitHub Copilot Max | $100 / month | Intensive agent work |
| Claude Opus 5.5 API | $4 / $20 per million input and output tokens | An expensive option; count the whole agent loop |
| Claude Sonnet 5.5 API | $2 / $10 per million input and output tokens | A steadier price-quality balance on typical tasks |
| Claude Haiku 5.5 API | from $0.10 / $0.50 per million for a short context | Simple tasks and fast processing, not a stand-in for a frontier model |
Sources: Cursor pricing, GitHub Copilot plans, Anthropic API pricing.
Subscriptions have quotas and spending rules. An API bills by tokens and has cache quirks. A long context, retries, and tools can change the cost of one task several times over. You cannot compute that cost reliably from the price of a million tokens alone.
A short list for a trial, not for a blind purchase:
- A hard terminal task: start with a run of Claude Code and Opus 5.5.
- Agent work on OpenAI models: compare Codex on the same tickets.
- A cheap subscription in a familiar IDE: GitHub Copilot Pro at $10, if the included credits are enough.
- An editor with its own models and several usage levels: Cursor, with the plan chosen by intensity and by the need for third-party models.
- API savings: Sonnet 5.5 on typical tasks, Opus 5.5 where the extra quality actually cuts the number of rounds.
- Company code: your tickets, tests, SQL procedures, and multi-file edits. One ranking is too little.
Where the figures come from and where they break
Prices and boards in this section were checked against pages available on the preparation date. A bench can update without a product rename. A maker publishes its own results. An independent run can use a different harness and different limits. For an editorial publication, keep the check date and do not merge percentages from different tests into one table.
Product pages worth keeping nearby: Cursor, Windsurf, GitHub Copilot, Antigravity, JetBrains, Zed, Claude Code, Codex, Gemini CLI, Aider, Continue, Cline, Roo Code, OpenHands, Replit. Benches: SWE-bench, SWE-bench Pro, LiveCodeBench, the multi-language patch board, Artificial Analysis, the open model board.
Before the next edit of this snapshot, refresh subscription and API prices, version names, the latest results with mode and harness named, availability in the countries you care about, and the licenses of open weights.
Two tools missing from the first table
These two names belong in a comparison with Cursor. They solve different jobs. OpenCode is an open agent with a choice of provider. Kimi Code is a coding service around Kimi models, available through a CLI, a desktop app, and a VS Code extension.
A shell that is not tied to one model
OpenCode is distributed as a coding agent with open source. You run it in a terminal, in a desktop app, or as an IDE extension. The project is under the MIT license and supports many providers and local models. Check the exact integration list against the current docs. Pages: the project site, the repository, OpenCode Zen, OpenCode Go.
You can change the model without changing the agent motion you already know. One repository is a convenient place to compare several providers. There is a terminal and an IDE tie-in. A cloud API and a suitable local model both fit. Open code lets you read the implementation and fit the process to the team.
The project also has its own ways to reach models. OpenCode Zen is a curated catalog of models the team checks on agent scenarios. Payment depends on use. OpenCode Go is a $10 monthly subscription for inexpensive access to selected open-weight models. A free client does not make every connected model free.
Quality is set by the model, the setup, and the tools, as well as by the shell. Your own API key can run the bill up through a long context and repeated loops. Before you connect, check which data goes to the chosen provider and what stays in session history.
A service around one model family
Kimi Code belongs to Moonshot and can read code, change it, run commands, and carry agent work. It is available through a CLI, a desktop app, and a VS Code extension. You can use it on its own or connect Kimi models to compatible agent environments, including OpenCode and Claude Code, when the current instructions and the account terms allow it.
Pages: the service docs, the plan description, the model page.
OpenCode chooses among many providers. Kimi Code first of all gives access to Kimi models and the Kimi service. The docs name coding models in the family, including ones with a large context and faster output. The model identifier, the context limit, and the reasoning level depend on the subscription and they change.
The Kimi K2.7 Code page lists example prices under annual billing:
| Kimi Code plan | Price per month | How it is positioned |
|---|---|---|
| Moderato | $15 | Regular work with weekly limits |
| Allegretto | $31 | Denser development and more parallel tasks |
| Allegro | $79 | Hard projects and a higher load |
| Vivace | $159 | The highest limits |
The API price on the same page for kimi-k2.7-code is $0.95 per million uncached input tokens, $0.19 per million when the cache hits, and $4 per million output tokens. That is the rate for this version, not for every Kimi model. Check region, account type, and payment method.
The service is interesting for the ratio of cost to capability, for a large context window on supported configurations, and because you can try the model in its own client and in a compatible agent. Some plans are counted for coding load, not for a general chat. The vendor's stated characteristics do not guarantee the same result on your project. Check speed, weekly limits, CLI stability, agent compatibility, and availability in your country.
Three columns on one task set
| Criterion | OpenCode | Kimi Code | Cursor |
|---|---|---|---|
| Idea | An open agent with model choice | A service around Kimi models | An editor with an agent |
| Tie to a provider | Low: many providers and local models | Higher: focus on Kimi models | Medium: a model set and plan rules |
| Environment | Terminal, desktop app, IDE | CLI, desktop app, VS Code extension, compatible agents | Its own IDE on a VS Code base |
| Payment | Free client; model, API, Zen, or Go separate | Subscription or API | Subscription and included-usage rules |
| Main gain | Freedom to compare models | Access to Kimi models and their context | A whole daily workflow |
| Main risk | The bill and the model choice sit with you | Limits, availability, and quality depend on the plan | Dependence on pricing policy |
| Closer fit | Someone who wants to run the model set | Someone who wants to try Kimi on their tasks | Someone who wants a whole IDE with little setup |
For an honest test, take one task set and one commit. Compare Cursor with a chosen model, OpenCode with the same model if both tools have it, OpenCode with another model through another provider, Kimi Code on a suitable plan, and if you need it a Kimi model connected to OpenCode when support and the account allow that. That separates the model's effect from the agent's and the IDE's. Measure test success, patch quality, retry count, time to an accepted result, and full cost. Different models on different tasks do not yield that conclusion.
A preliminary reading, with no right to a crown: OpenCode is interesting as a flexible shell for experiments and provider control. Kimi Code belongs in the budget and middle band, so you can try Kimi models on real tasks. Cursor remains a convenient reference point for a whole IDE.
Frequently asked questions
How is Cursor different from a chat with a model?
Cursor is an editor with a repository index, multi-file edits, and an agent mode. A chat without an environment offers a fragment. You still have to carry that fragment to a checked patch in the project.
Can you pick a tool from one ranking?
Only when that ranking measures the skill you actually need. A Terminal-Bench percentage speaks about multi-step work in a terminal. It is silent on autocomplete and on a simple interface edit. The decision needs a run on your own tickets.
Which is cheaper, a subscription or an API?
It depends on how dense the work is and on how many rounds the agent takes. A subscription is more predictable for one person's daily load, while you stay inside the limit. An API shows the price of rare and very long tasks more honestly. Compare the cost of an accepted task over two to four weeks, not the monthly number on the landing page.
When is a local model justified?
When data policy keeps code off an external API, and when the hardware can carry the size you need. You still measure quality and speed on your languages. Open weights by themselves do not equal a closed frontier model, and they do not zero the bill for GPU and team time.
How do you separate the model's effect from the environment's?
One commit, one task set. First the same model in two tools. Then another model in the same shell. If you change the model, the tasks, and the environment at once, the claim about a "best tool" falls apart.
Which figures in this article will go stale first?
Prices, version names, and places on the boards. Tool classes and the way you count the cost of an accepted task live longer. Before a purchase, open the vendor's page and the date on the results board.
What should a team pilot record?
The share of tasks without a substantial rewrite, time to the first correct solution, number of runs, regressions, cost of an accepted change, people's satisfaction, and fit with security rules. Participants are better off not knowing which model solved the task, when you can hide that.
Does a browser environment fit a company repository?
Replit is strong in the write-run-show loop for a prototype and for learning. A mature company repository with strict access and audit usually stays in a local or managed IDE. The decision follows data policy, not demo convenience.
Further reading
Nearby on this site, these pieces cover the questions that sit next to this choice.
- How AI IDEs work with code — index, context, and the suggestion, chat, and agent modes.
- Token economics of coding with a model — what one loop's bill is made of.
- Code review in the AI era — what a person should look at in an agent's patch.
- Agentic engineering — how a team puts an agent into the process.
- Spec-driven development — which file has to be in the input before the agent edits.
- An eval harness for agents — how to check output in a repeatable way.
- Secure AI development — data, permissions, and the agent's boundary.
- The programmer in the AI era — which work stays with the person when generation got cheaper.
Conclusion
The bench worth keeping closes the cabinet door on the second try and does not eat the whole board on rework. A different saw can still hang on the poster.
For a daily IDE, compare Cursor, Windsurf, and VS Code with Copilot on your repository. For autonomous engineering tasks, run Claude Code, Codex, or Gemini CLI on the same tickets. For model choice and API control, look at Aider, Continue, Cline, and Roo Code. For local control, try Qwen Coder and other open weights on your hardware. For company ERP, put correctness, verifiability, security, and the cost of an accepted result first.
One step is enough this week. Take fifteen real tasks, two tools, and one commit. Write down the share of accepted patches and the full cost. That sheet is more honest than any place on someone else's board.
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