The AI Model Rankings Put Gemini 4 Carbon First By One Point—Here’s The Context

🔍 Read the full analysis: The AI Model Rankings Put Gemini 4 Carbon First By One Point—Here’s The Context on ThorstenMeyerAI.com

Google’s Gemini 4 Carbon leads the new Artificial Analysis Intelligence Index v5 with 59 points, one point above Anthropic’s Claude Opus 5.5 and two above GPT-6 Astra. The result puts Carbon at the top of this nine-test ranking, but does not establish it as the best model for every task—and Google has not made it publicly available.

Artificial Analysis launched version 5 of its index on October 27, rebuilding the leaderboard around nine tests grouped into four equally weighted areas: knowledge work, coding, science and general capabilities. The scores reported for the leading models are 59 for Carbon, 58 for Claude Opus 5.5, 57 for GPT-6 Astra and 55 for GPT-6.1 Sol. Google’s previous model, Gemini 4 Argon, scored 50.

Carbon placed first on four of the nine tests, according to the source report: hard reasoning and knowledge questions, science workflows performed in a terminal, a test that penalizes incorrect answers, and business workflows across software tools. Its other results were not leading scores. Claude models led office-style knowledge work, Claude Opus 5.5 led the cited real-world coding test, GPT-6 Astra led a physics research test, Kimi K3.1 Pro led terminal tasks, and Meta’s Muse Wonder Full led document reasoning.

The index report puts Carbon’s cost at $7.76 per index task at standard prices. That is below the cited $10.04 for Claude Opus 5.5 at maximum reasoning, but above GPT-6 Astra’s $6.24 and well above GPT-6.1 Sol’s $1.27. The source also lists introductory API pricing of $2 per million input tokens and $10 per million output tokens, rising later to $4 and $20 respectively. Those prices do not mean every task will cost the same; actual expense depends on usage and task length.

A narrow lead with practical trade-offs

A one-point margin makes the ranking useful as a comparison, not a decisive purchasing rule. The index gives equal weight to four broad areas, while individual tasks within them have different weights. A company whose main work is office documents or software development may care more about the tests where Carbon trails than about its wins in science or cautious factual answering.

The reported cost figures also show why buyers may want to compare cost per completed task, rather than only model prices or leaderboard position. A model that costs less per benchmark task may not produce the best result for a company’s particular workflow, and a higher-priced model may be worthwhile if it reduces errors or follow-up work. The source report recommends testing models on organizations’ own tasks before switching; the leaderboard alone cannot settle that decision.

For everyday users, there is no immediate change because Carbon is not publicly available. If it reaches consumer products later, the report expects its differences to matter most on longer, harder tasks. That is an expectation, not a verified account of public user experience: access and product details remain pending.

Popular right now

KitchenAid Classic Series 4.5 Quart Tilt-Head Stand Mixer K45SS, Onyx Black

AmazonView Latest Price

Ninja DualZone Air Fryer 10 Qt Smart 6-in-1 Grey (DZ950)

AmazonView Latest Price

Dyson Cyclone V10 Origin Cordless Vacuum

AmazonView Latest Price

As an affiliate, we earn on qualifying purchases.

What version five measures

Artificial Analysis describes the index as a combined score from nine benchmark tests, rather than a single exam. In v5, knowledge work, coding, science and general tasks each make up 25% of the total. Within those categories, the tests have their own weights; for example, AA-Briefcase accounts for 15% of the index, as does FrontierCode 2.0, while several other tests carry smaller shares.

The source report says 60% of v5 uses private test sets or held-out answers, intended to reduce the chance that a model has memorized test questions. Many tasks also involve extended work, such as operating in a terminal, producing reports or fixing code. That can offer a different picture from short chat prompts, but it also means the combined result depends on the chosen tasks, their weights and the model settings used.

The difference between Carbon’s knowledge accuracy and its score on AA-Omniscience illustrates how a benchmark’s scoring rule can shape results. Carbon answered 58% of questions correctly in the figures supplied, below several Claude and GPT models. Yet it scored 54 on the test’s measure because the scoring penalizes wrong answers and Carbon was reported to have an 11% hallucination rate. The source gives rates of about 50% for Claude Opus 5.5 and GPT-6 Astra on that measure. That is a result on this test, not a general guarantee about accuracy in all settings.

„“Choose by task and by cost per finished task, not by rank.”“

— Thorsten Meyer, source report

Access and benchmark limits remain

Google has not provided a public release date in the supplied material. It says rollout will start with a subset of cyber defenders, then move to trusted partners, developers, businesses and consumers. The timing and conditions for each stage are not specified.

The supplied report also does not provide the full results table or the complete discussion of the index’s limitations; its source text cuts off during a coding comparison. The reported scores and rankings should therefore be understood as the figures presented in that material. The index measures selected tasks at particular model settings. It does not establish how Carbon will perform across every real-world use, nor whether results will hold under different settings, tools or workloads.

Google’s staged rollout is next

The next stated milestone is access for a subset of cyber defenders, followed by trusted partners and then broader groups, including developers, businesses and consumers. Google has not announced dates for those steps in the supplied report. Further information on availability, API access and pricing changes will clarify when organizations can test Carbon directly.

For readers comparing models, the immediate next step is not to treat the index as a universal verdict. The ranking offers a snapshot of performance across its selected tests. Until broader access and more task-specific results are available, it remains unclear how often Carbon’s benchmark strengths will outweigh its weaker scores and higher costs for particular users.

Key Questions

What does Gemini 4 Carbon’s score of 59 mean?

It is Carbon’s combined score on Artificial Analysis Intelligence Index v5, which combines nine tests across knowledge work, coding, science and general capabilities. It is not a score that measures every possible use of an AI model.

How far ahead is Carbon?

Carbon scored one point above Claude Opus 5.5 and two points above GPT-6 Astra in the figures reported. The narrow margin does not show that Carbon is better on every test; the source says it placed first on four of nine.

Can the public use Gemini 4 Carbon now?

No public availability is reported. Google plans to begin with a subset of cyber defenders, then expand access in stages, but no public release date is given.

Is Carbon the most accurate model on the knowledge test?

Not according to the figures in the source report. Carbon answered 58% correctly on the cited test, while several Claude and GPT models had higher plain accuracy. Carbon scored better on the test’s measure because it was reported to make fewer incorrect guesses.

How much does Carbon cost?

The report lists $7.76 per index task at standard prices and introductory API rates of $2 per million input tokens and $10 per million output tokens, later rising to $4 and $20. Actual costs depend on usage, and the report does not specify a public release date.

Source: ThorstenMeyerAI.com