GPT-6 Astra vs Claude Fable 5.1 vs Gemini 4 Carbon: which should you choose for coding, research and writing?

Whenever a new large language model launches, people tend to ask the same question: is it more capable than the previous models?

In our day-to-day work at API Hop, we care more about something else: can the new model complete the same job better and faster? How long will we wait? What will it cost? Will a person still need to revise the result?

For GPT-6 Astra, Claude Fable 5.1 and Gemini 4 Carbon, we look at general capability benchmarks, coding agent results, fixes in real projects, and tests involving long tasks, writing and website creation.

The main takeaway: different tasks need different measures

Our conclusion is that models perform differently in different business contexts.

The basics of the three models

First, here is the basic information supplied by the providers.

One distinction is easy to miss: the model and the tool you use to run it are not the same thing. For the same coding task, running a model through Codex, Claude Code or Antigravity CLI can change how it reads files, executes commands, manages context and retries. What you see is really the result of the “model + tool + configuration.”

That difference may be less noticeable when polishing a paragraph. When an agent works in a large repository for several hours, its tool environment often matters a great deal.

General capability: Carbon leads this benchmark, but not necessarily every task

We start with a general evaluation from the same benchmark version.

Carbon leads this version of the Intelligence Index. Google’s new model deserves attention, especially for complex reasoning, knowledge and science tasks.

Astra’s average API cost on this task set is lower than that of the other two listed settings.

Fable’s lower overall score does not mean it is weaker at every kind of work. The knowledge-work evaluation below tells a different story.

Choosing a coding model: patch quality and actual cost matter more

Start with coding agent results from the same benchmark version

Everyday development involves implementing features, fixing bugs and delivering changes in a repository. Coding agent evaluations are a useful way to examine that work.

The Carbon combination scores highest. Astra + Codex is faster and has a lower average API cost in these results. Fable + Claude Code scores between the two, but costs more and takes longer.

64 coding tasks: Fable solves more, but the bill is higher

Compared with an overall score, we find imec/aistack’s test closer to developers’ practical questions: was the bug fixed, was the feature completed, and what did it cost?

There is a useful trade-off here: Fable solved 17 more tasks for about €68 more in total. If completing difficult tasks is critical to a project, those 17 solutions may matter more than the savings. If you handle large numbers of simple tasks every day, cost and speed may carry more weight.

The same Gradle bug: both agents fixed it, so why prefer Fable’s patch?

Another example is smaller, but very close to everyday development.

Gradle’s engineering blog asked two agents to fix the same documentation configuration issue, using the same repository and task constraints. The result: both passed.

Why did the developer prefer to merge Fable’s changes? Looking at the differences, Fable changed a more specific part of the configuration, followed the repository’s existing style more closely and added a regression test. Astra’s patch fixed the current issue, but applied more broadly, which could have unexpected effects when similar objects are added later.

For us, the lesson goes beyond finishing a couple of minutes sooner: completing the task is only the starting point. Check whether the changes reach too far, whether regression tests exist and whether the code will be maintainable.

More reasoning is not always better for code review

We have made this point when discussing other models, and have also seen it in our own development work: more reasoning does not always improve the result.

In this code review workflow, High was slower without improving the overall metrics. Do not assume that a setting labeled High or Max is worth the extra time and cost. Test the effect in your actual workflow.

Research and information work: producing a result does not make it useful

Fable moves ahead in a knowledge-work evaluation

Let’s change the type of task and see what happens.

This is precisely why we advise against relying on a single overall leaderboard: the same models rank differently when the task changes to knowledge work.

If you need to organize customer research, write reports or prepare work materials, Fable is worth putting near the top of your trial list.

Even a $10,000 research budget can end in rejection

CRUX ran a long-task evaluation worth reading. It gave Astra and Fable 5.1 research questions from unpublished papers, a $10,000 research budget, and asked the original researchers to review the resulting papers.

The report showed progress in experimental design and resource use, but experts clearly rejected both final papers. The research team identified problems with research judgment, originality, communication and changing direction after failure. The models worked on different research questions, so their final ratings should not be treated as a fair head-to-head comparison. CRUX research report

We think this is a useful lesson for anyone using AI for research.

For example, if we ask a model to research a SaaS competitor, we need more than 20 pages of text. Are the quoted prices real? Are the features current? Where does its traffic come from? Are its latest features worth learning from?

Working longer can produce more text, but it does not guarantee the right direction. The result may simply be a large amount of unusable material. Writing the requirements in advance and asking the model to fill in a defined structure gives us more control over the work.

Carbon’s five-hour tasks also had delivery problems

FrontierCS gave models up to five hours per task to restore methods removed from research-paper implementations or submit a better method.

In this third-party test, several runs ended before submitting a result because of how the agent handled background tasks.

Among tasks for which both models submitted results, Carbon had the higher average score. The point here is simply that an agent capable of running for hours may still misunderstand the goal or fail to deliver the final result.

Writing and websites: what do public examples tell us?

The same material: Fable structures it better, Astra finishes the workflow faster

During our research work, we gave Fable 5.1 and Astra the same 20-page PDF of source material and asked each to turn it into a long article.

We preferred Fable’s opening and organization: the structure was clear and the reasoning coherent. Astra, however, completed the whole workflow faster.

When using a model to write an article, we recommend preparing organized source material, or at least deciding on a rough outline and structure first. That gives you a basis for judging whether the output meets your needs.

Website creation: a score is not traffic or conversions

The WebDev Arena page snapshot from October 27, 2026 showed these scores:

Source: Original WebDev Arena leaderboard.

Building a website combines development, writing and adapting content to search engine requirements. Although we include WebDev Arena scores here, our recommendation at API Hop, based on our extensive website-building experience, is to use Fable to plan the content and Astra to develop the site.

Pricing: the same token rates can produce different bills

We often see people compare prices per million tokens and simply choose the lowest. That number matters, but it is not enough to judge the cost of complex tasks involving multiple reasoning rounds, tool calls and retries.

Start with official standard API rates

The unit below is US dollars per million tokens. These are not API Hop platform prices.

Two details can also affect your bill:

• Astra: When a single request exceeds 272K input tokens, input and cache rates are multiplied by 2 and output rates by 1.5 for the entire request, not just the portion above the threshold.
• Fable 5.1: The provider says its 1M context uses standard input and output rates. Cache writes have separate charges that also depend on storage duration.

If you regularly fill the context with large repositories, long documents or extensive conversation histories, pay close attention to these rules to avoid a surprising bill.

A fixed-usage calculation

Suppose a request uses exactly 100,000 uncached input tokens and produces 10,000 total billable output tokens. Leave out tool fees, cache write fees, retries and special plan pricing for now.

The calculation is straightforward: input tokens / 1,000,000 × input rate + output tokens / 1,000,000 × output rate.

We hold token counts constant in this example. In real use, models may spend different amounts on reasoning, produce outputs of different lengths and retry different numbers of times.

Cost per accepted result is a more useful measure

When a task has clear boundaries and acceptance criteria, we can use:

Average cost per accepted result = API and tool costs for all runs ÷ number of tasks that pass acceptance checks

Total costs must include failures and retries. Separately record how much human revision each successful result needs. A model that saves $2 but requires another half-hour of editing may be a very different proposition.

API Hop’s model pages list platform prices and official reference prices. We recommend checking them separately and following the actual billing rules. Platform prices are not official API prices. API Hop Astra page · API Hop Fable page · API comparison

How would we test these models on our own tasks?

We recommend breaking real work into small tasks with clear acceptance criteria. Start with a minor bug or a small feature.

To make the process reproducible, we would prepare the following three task types. This is a test plan, not the results of an experiment we have already completed.

Run each task type three times with every accessible model. Astra and Fable can go through an initial round first. Until Carbon has a confirmed, reproducible public testing route, we will leave its results blank and never substitute Argon for Carbon.

To make the data useful, we would fix the source-material version, task instructions, time limit and budget. Each run would start in a new session and record the exact model ID, reasoning setting, tool version, any fallback, cost, runtime and final output. For writing, focus on factual errors and human revisions. For bug fixes, focus on tests and the scope of changes.

For a text comparison of your own, you can start with this simple template:

Use the factual material below to write an accessible article for general readers.

Audience: people learning about this issue for the first time
Task: explain the issue, give practical steps and state when they apply
Must cover: [list 3–5 key points]
Do not: add data missing from the material, invent testing experience or exaggerate conclusions
Output: article text, sources for the facts and information that still needs confirmation

Material:
[paste the same source material]

One principle matters: do not keep only the best-looking output. Failed tasks, repeated calls and human fixes are all real costs of using a model.

Frequently asked questions

Can everyone use Gemini 4 Carbon now?

As of October 30, 2026, that is not the case. Google says access starts with selected cyberdefense organizations and will gradually expand to trusted partners. Testing by research organizations does not mean every developer has ordinary public API access. We will update this information based on official access announcements and the platform’s actual model catalog. Google’s official announcement

If Astra and Fable have the same input and output rates, why can task costs differ so much?

They can use different numbers of tokens, have different cache hit rates and reasoning effort, and make different numbers of tool calls and retries. Even when both charge $10 for input and $50 for output per million tokens, fixing the same bug need not cost the same. The Gradle example cost $3.54 and $4.34 respectively. Gradle test

Is Fable 5.1 Max always better than a lower reasoning setting?

We have not seen evidence supporting that absolute claim. In CodeRabbit’s tests on the same set of code reviews, a higher reasoning setting was slower and had lower overall recall. We recommend first using a setting that meets your acceptance criteria, then deciding whether more reasoning is worthwhile. CodeRabbit test

Which model is best for long articles in Chinese?

We are not giving a firm three-way ranking at this point. Writing examples and knowledge-work benchmarks exist, but they do not form a three-model test with the same material, environment and Chinese writing criteria. We suggest using the same Chinese-language material and comparing factual accuracy, structure, tone and editing time.

Why do evaluation sites rank models differently?

Their task sets, scoring methods, reasoning settings, agent tools and cost definitions can differ. General knowledge tests, fixes in real repositories and business-document preparation are different jobs. At minimum, check the benchmark version, test date, model settings and passing criteria before relying on a result.

Can I compare all three models on the same task directly in API Hop?

Start with API Hop’s current GPT-6 Astra, Claude Fable 5.1 and model catalog pages to check available models and prices, then compare outputs using the same text task. We do not currently present Carbon as a model that ordinary accounts can call directly. Also, an online text tool is not the same as a full agent environment with code execution, file uploads and web browsing. Confirm tool capabilities separately when comparing coding agents.

What would we choose?

Putting these public results together makes our selection process simpler.

For bug fixes in an existing repository, we would first test Astra and Fable on the same task in our own project. We would examine completion, regression tests and future maintenance cost, not just which model outputs code first.

For organizing information, writing reports or extended research, we would pay closer attention to Fable’s knowledge-work results and consider Carbon’s new evaluations as a direction for later tests. Every model still needs fact-checking and clear acceptance criteria.

For ongoing article or landing-page production, we would first assess whether the output meets the content requirements and how long each piece takes to revise, rather than letting an overall benchmark decide the entire workflow.

Our most useful comparison is: acceptable delivery + total cost + wait time + human revision effort.

To try a real text task, API Hop provides model details, pricing and trial entry points for Astra and Fable 5.1. The API comparison page also shows current models and conditions. We provide model services, but the third-party tests cited here are not API Hop experiments. We will keep the sources and testing limitations visible so readers can judge for themselves.

Sources and update notes

Source review date: 2026-10-29. The leaderboards cited here are the versions available at that time. Prices, model access and evaluation results may change. When there is new official access information or reproducible testing of our own, we will record those changes separately rather than silently replacing old test conditions with new data.

Official models and pricing

• OpenAI: GPT-6 Astra specifications and billing
• Anthropic: official Claude Fable 5.1 model page
• Google: Introducing Gemini 4 Carbon, 2026-10-28

Independent evaluations and examples

• Artificial Analysis: Carbon results in Intelligence Index v5.0
• Artificial Analysis: Astra and Fable in general capability and knowledge work
• Artificial Analysis: Antigravity CLI vs Codex
• Artificial Analysis: Claude Code vs Codex, including Fable 5.1 results
• imec/aistack: 64 coding tasks and actual bills
• Gradle engineering blog: two fixes for the same documentation bug
• CodeRabbit: Fable 5.1 code review test
• CRUX: open-ended research tasks with Astra and Fable
• FrontierCS: Gemini 4 Carbon on five-hour research coding tasks
• Aaron Makelky: writing and website experience with Astra and Fable
• WebDev Arena: website creation leaderboard

Editorial note: “We” refers to the API Hop team’s source review, analysis and usage recommendations. It does not mean we personally repeated the third-party experiments. All example cost calculations state their assumptions. Scores and costs from different evaluation systems are not added together or directly converted.