Gemini 4 Carbon vs. Argon vs. Claude Haiku 5.5: Which AI Model Should You Use—and What Does It Cost?

October 2026 brought three new AI model names in just three weeks, leaving freelancers with a practical question: Which model is right for which job?

Anthropic launched Claude Haiku 5.5 on October 7, Google introduced Gemini 4 Argon on October 13, and Google followed with Gemini 4 Carbon on October 28. But there is a major difference in availability: only Argon and Haiku 5.5 can currently be accessed through public APIs. That fact shapes most of this comparison.

Short version: Argon for Google-platform work today, Haiku 5.5 for high-volume repetitive work, Carbon to watch but not yet publicly available.

The Three Models at a Glance

Gemini 4 Argon is Google’s first Gemini 4 frontier model. It is generally available through the paid Gemini API as gemini-4-argon. It supports a 1-million-token context window, multimodal input, text output, function calling, structured outputs, code execution, and Google Search grounding.

Gemini 4 Carbon, announced on October 28, 2026, is described by Google DeepMind as its strongest model, with state-of-the-art cybersecurity and long-horizon capabilities. Google has not published a public model ID, developer API date, or Carbon model page. Access began with a subset of cyber defenders, followed by trusted partners through the Fairwind Program. Broader access has no date yet.

Claude Haiku 5.5 is Anthropic’s smallest and fastest current model, designed for high-volume, latency-sensitive tasks such as classification, routing, extraction, and sub-agent work. It has a 1-million-token context window, supports up to 128,000 output tokens, and offers adaptive thinking controlled by an effort setting. It runs on the Claude API, major clouds, and GitHub Copilot on paid plans.

API Pricing: How Much Does Each Model Cost?

List prices below were published in late October 2026; check each vendor’s pricing page before building.

Argon’s introductory pricing ends on November 30, 2026. Starting December 1, its input and output prices double to $4 and $20, while cached input rises to $0.20.

Carbon launches at the same introductory price as Argon, with cached input discounted by 95% to $0.10. Its later price is also $4 input and $20 output, but Google has not announced when Carbon’s introductory period ends. Carbon is therefore not cheaper than Argon. Once it becomes available, the choice between the two Gemini models will be about capability rather than token price.

Haiku 5.5 is in a different pricing category, with one important catch. Prompts up to 100,000 tokens cost $0.10 input and $0.50 output — one-twentieth of Argon’s introductory price. Above 100,000 tokens, the entire request is billed at $0.50 input and $2.50 output. Even at that tier, Haiku 5.5 remains four times cheaper than Argon.

For example, 10 million input tokens plus 1 million output tokens would cost $30 at Argon’s introductory rate, $1.50 at Haiku 5.5’s short-prompt rate, or $7.50 at its long-prompt tier.

Capability: Where Gemini 4 Carbon Pulls Ahead

On Google’s published results, Carbon beats Argon on every benchmark Google has published for both models. The Artificial Analysis Intelligence Index v5.0 places Carbon first at 59, compared with Argon’s 50.

The largest gaps appear in cybersecurity and long-context work:

• ExploitGym: 54.8% for Carbon versus 42.0% for Argon.
• GraphWalks long-context retrieval: 96.2% versus 84.2%.
• Terminal-Bench-Science: 64.8 versus 52.9.
• Agentic coding, Terminal-Bench 4.0: 65.2% versus 57.1%.
• SEC-bench Pro vulnerability detection: 88.2% versus 83.4%.

For agents running unattended, Google reports a Gray Swan prompt-injection success rate of 0.0% for Carbon, compared with 0.7% for Argon. Google also reports that Carbon performs more than twice as well as Argon on an internal secure-code benchmark.

These are vendor-reported results, not independent evaluations. Early testers also called Carbon weak at frontend and UI output, so test your own prompts on Argon first for UI work.

Haiku 5.5 is not a frontier-benchmark model; it is for tasks repeated thousands of times, where a small quality difference matters far less than a twenty-times price difference.

Which Model Should You Use for Each Job?

Shipping a client project on Google’s platform this week: Choose Argon. It is the only Gemini 4 model with a public API, a published model ID, and published specifications. Put gemini-4-argon in a configuration variable rather than hard-coding it throughout your code. That makes a future switch to Carbon a one-line change plus testing.

Security work, penetration testing, vulnerability triage, or secure code review: Carbon is the model to watch. If you qualify for the Fairwind Program, apply through that route. Otherwise, plan around Argon and retest your work when Carbon becomes available.

High-volume daily tasks: Start with Haiku 5.5. It can handle support-inbox classification, lead scoring, document extraction, chatbots answering recurring questions, or sub-agents summarizing threads before a larger model acts. Keep prompts below 100,000 tokens where possible, because crossing that threshold multiplies the bill by five for the entire request.

Three Costly Mistakes to Avoid

1. Do not assume Carbon’s parameters match Argon’s. When Argon became generally available, it dropped explicit caching and ignored temperature. Recheck the model page when Carbon is released.
2. Do not reuse old Haiku token counts. Haiku 5.5’s new tokenizer counts roughly 30% more tokens. A prompt measured at 77,000 tokens on Haiku 4.5 could exceed the 100,000-token threshold on Haiku 5.5.
3. Do not budget for Argon’s introductory price after November 30, 2026. The price doubles on December 1.

Pick five real prompts, run them on Argon and Haiku 5.5, record pass rate, response time and tokens, then rerun them on Carbon when it gets a model ID.

Frequently asked questions