LongCat 2.5 Released: Pricing, Benchmarks, and How to Use It

Meituan’s latest AI release combines visual understanding with extended coding workflows. Its launch pricing makes it worth evaluating, but developers should look closely at the benchmark conditions and API limits.

Meituan has released LongCat-2.5, a multimodal AI model aimed at software development and tasks that require multiple steps, tool calls, and sustained reasoning.

The technical announcement is dated November 1, 2026. The official API changelog lists the model as available on November 2, following the September release of LongCat-2.5-Preview. Older Preview reviews should therefore be read separately from evaluations of this release. Official release history

Meituan says training ran entirely on Chinese domestic computing infrastructure. Its release coverage also describes access through the LongCat website and API. That makes this both a model launch and a development in the infrastructure behind large-scale AI. Meituan release coverage

For founders and builders, the immediate question is practical: can LongCat-2.5 complete useful work at a competitive total cost?

This guide explains the published evidence, launch pricing, and a focused way to test it.

What Is LongCat-2.5?

LongCat-2.5 is a mixture-of-experts model with 1.6 trillion total parameters and approximately 48 billion activated per token, according to its technical report.

The team describes two main advances: stronger visual understanding and improved execution of extended engineering tasks. Its visual architecture represents images using discrete tokens, while its training approach emphasizes long sequences of actions and reinforcement learning. LongCat technical announcement

In practical terms, the intended workflow goes beyond asking a chatbot for a function. A developer might ask an agent to inspect a project, plan changes, edit several files, run checks, and respond to failures.

Those capabilities depend on the surrounding agent software, too. A model answering an API request does not automatically have access to your terminal, repository, browser, or deployment tools. The application using the model must provide those capabilities and enforce their permissions.

LongCat-2.5 API Specifications

The official quick start provides the following integration details:

Account registration and an API key are required. Official API quick start

Context length and output length are different limits. A large context window concerns the information available to a request. It does not mean the model can produce an equally long response or reliably use every detail supplied.

There is also a launch-documentation distinction worth preserving: the research report describes some evaluations with a 256K generation budget, while the API quick start lists 128K maximum output. A research configuration should not be treated as a promise about the public API. Evaluation conditions, API limits

What Do the Benchmarks Show?

LongCat’s report publishes these selected results:

These are vendor-reported scores. The report specifies different harnesses and evaluation settings across tasks, and its comparison table mixes externally reported competitor scores with in-house measurements. Benchmark table and methodology

The published improvements make LongCat-2.5 worth investigating. They do not establish that it will outperform your current model on your application.

A fair comparison needs more than a score:

• Task conditions: Did each model receive the same instructions and files?

• Tools: Could each model run the same commands and access the same resources?

• Budget: Were time, token usage, and retries comparable?

• Verification: Did the result pass meaningful checks?

• Human effort: How much correction did the developer provide?

A model that produces a convincing explanation but leaves a broken implementation has not completed the task.

LongCat-2.5 Pricing

At publication, LongCat lists these limited-time discounted API prices:

The pricing page says rates may change and that platform prices and billing records are authoritative. Official pricing

A Simple Cost Example

Using those published rates, a hypothetical workload with:

• 1 million uncached input tokens

• 100,000 output tokens

would have a token charge of:

$0.30 + $0.12 = $0.42

That is illustrative arithmetic, not a measured cost for completing a coding task. It excludes additional requests, tool services, hosting, and other charges.

Agent workflows can revisit the same files, retry failed actions, and generate substantial intermediate work. A low token price is useful, but the business metric is cost per accepted result.

For example, compare two implementations by adding their API charges to the time required to review and repair them. The cheaper request may become the more expensive result if it creates substantial rework.

Guide 1: Make Your First LongCat-2.5 API Request

Start with a small text request before connecting the model to a repository or automation workflow.

Step 1: Create an API Key

Register through the official LongCat platform and open its API Keys section.

Use a dedicated key for your experiment. Keep it outside source control and client-side application code.

Step 2: Set the Key in Your Environment

Make your LongCat key available as the environment variable:

LONGCAT_API_KEY

Use your terminal’s secure input method or your usual secret manager. Avoid putting the actual key in a command that will remain in shell history.

Step 3: Send a Small Test Request

The following example follows LongCat’s documented Chat Completions request format. It uses an original test prompt and explicitly disables thinking for a short connectivity check. Chat Completions reference

curl --fail-with-body \
  --request POST \
  "https://api.longcat.chat/openai/v1/chat/completions" \
  --header "Authorization: Bearer ${LONGCAT_API_KEY}" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "LongCat-2.5",
    "messages": [
      {
        "role": "user",
        "content": "List three checks to perform before releasing a small web application. Keep the answer under 100 words."
      }
    ],
    "max_tokens": 512,
    "thinking": {
      "type": "disabled"
    }
  }'

This command is intended for a Bash or Zsh terminal with curl installed.

The example is documentation-based. Crowdstax has not executed an authenticated request.

Step 4: Check the Response

Inspect the HTTP result and JSON response. A successful request should include the generated message under choices.

If it fails:

• Verify the key and account balance or quota.

• Confirm the model identifier is exactly LongCat-2.5.

• Check the endpoint and request body.

• For HTTP 429, reduce request frequency and use bounded exponential backoff, as the quick start recommends. Rate-limit guidance

Do not keep retrying a failing request without inspecting its error.

Guide 2: Test Visual Understanding With a Screenshot

LongCat’s visual API documentation supports image input through a public URL or an inline Base64 data URL. It also documents video input through URLs. Visual understanding documentation

A useful first test is a screenshot of a non-sensitive interface you own.

Choose a page with visible layout problems, then ask the model to separate observations from assumptions.

Suggested prompt:

Review this screenshot as a frontend engineer. List five visible layout or usability problems. For each, identify the relevant area, explain the evidence, and suggest a fix. Mark anything you cannot determine from the image as uncertain. Do not assume access to the source code.

This is more informative than asking whether the page looks good. You can compare each observation with what is actually visible.

For developers, this request structure follows the documented image format:

{
  "model": "LongCat-2.5",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Identify visible layout problems in this screenshot. Separate observations from assumptions."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://YOUR_PUBLIC_IMAGE_URL"
          }
        }
      ]
    }
  ],
  "max_tokens": 1500
}

Replace the placeholder with a publicly reachable image URL. Use the authenticated endpoint from the first guide.

This sends the image to the model provider. Use sample content rather than screenshots containing customer records, credentials, or confidential business information.

Test Implementation Separately

If the image review looks useful, try a second task in a coding environment:

Implement the approved layout changes in this sample project. Preserve existing behavior, avoid adding dependencies, and report which checks you ran.

Then inspect the actual page. A good screenshot critique and a correct implementation are separate achievements.

Guide 3: Connect LongCat to a Coding Workflow

LongCat publishes integration instructions for several coding tools. Its Claude Code guide describes an Anthropic-compatible endpoint and model configuration.

The distinction is straightforward:

The coding tool provides the working environment. LongCat provides the model response.

Changing the provider does not establish that every tool feature, permission flow, or integration behaves identically.

For a first evaluation:

1. Use a disposable copy of a small project.

2. Preserve your existing tool configuration.

3. Follow the provider’s current instructions for the installed tool version.

4. Confirm the active endpoint and model.

5. Begin with a read-only task.

6. Review proposed changes before permitting execution.

A useful opening task is:

Map the authentication flow in this sample repository. Identify the relevant files and explain how they connect. Do not edit files or run commands that modify state. List any assumptions you could not verify.

Check whether the explanation cites real files and accurately describes the code. If that foundation is wrong, giving the agent more autonomy will make the evaluation harder to interpret.

Guide 4: Compare LongCat With Your Current Model

For founders, a small evaluation built around actual work is more useful than a broad declaration that one model is best.

Choose three tasks with clear acceptance criteria.

Use the same starting project, instructions, and tool access. Give each model an independent working copy so one result does not influence another.

Record:

• Whether the task was completed.

• Whether the implementation passed review.

• Time to an acceptable result.

• API usage and charges.

• Number of human corrections.

• Unnecessary edits or dependencies.

• Failures outside the requested scope.

Run more than one attempt when practical. A single strong result can justify further testing, but it does not establish consistent reliability.

Calculate Cost per Accepted Result

A useful internal calculation is:

Total evaluation spend ÷ number of results accepted after review

Track developer review time alongside that figure. Keeping it separate makes the tradeoff visible without pretending every team values engineering time identically.

Does a 1M Context Window Mean You Should Upload Everything?

No.

A large advertised context window creates room for more information, but developers should still test whether additional context improves the result.

Start with:

• A clear task.

• Relevant files.

• Explicit constraints.

• Acceptance criteria.

Then add context when the agent demonstrates a specific information gap.

For example, supplying a service’s interface and failing test may be more useful than sending an entire repository that includes unrelated code and generated files.

Measure whether broader context improves correctness enough to justify the added processing and review effort.

Is LongCat-2.5 Open Source?

This article verifies hosted API access. It does not verify a downloadable LongCat-2.5 weight release or an accompanying model license.

Do not assume that a model can be self-hosted because an earlier model in the same family had an open release.

If self-hosting is essential, confirm the exact checkpoint, license, inference requirements, and supported deployment software before planning around it.

Is LongCat-2.5 Better Than Claude or Other Coding Models?

The launch evidence does not support a universal answer.

LongCat’s published results provide a reason to evaluate it. Your application still determines whether it is a better choice.

A model can be strong at repository navigation and weaker at preserving interface behavior. Another may produce more reliable changes while requiring a higher token budget. Availability, integration effort, and review time also affect the decision.

Compare completed work under matching conditions.

Avoid selecting a model from a single benchmark, an impressive demo, or an older Preview review.

What This Launch Means for Founders and Builders

LongCat-2.5 is worth watching because it targets a demanding intersection: interpreting visual information and completing extended software tasks.

The opportunity for a small team is to test whether another model can deliver acceptable work within its budget and existing workflow.

Begin with a bounded API request. Follow with a screenshot analysis and one reproducible coding task. Expand only when the results justify it.

A model launch becomes valuable to a founder when it improves something measurable: an accepted fix, a working interface, a shorter investigation, or less time spent correcting an agent.

Research and testing note: Information was checked against official LongCat and Meituan materials on November 2, 2026. Specifications, prices, and benchmark figures are attributed to their publishers. Crowdstax has not independently reproduced the benchmark results or executed the authenticated examples. Prompts, evaluation methods, and business interpretations are original editorial recommendations. Check the linked documentation before implementation because launch details may change.

Sources and Further Reading

1. LongCat-2.5 technical announcement and evaluation methodology

2. Official release history, including Preview and November availability

3. Meituan release coverage

4. API quick start, endpoints, and published limits

5. Official LongCat-2.5 pricing

6. Chat Completions request reference

7. Image and video understanding guide

8. LongCat’s Claude Code integration guide