Gemini 4 Carbon vs GPT-6.1 Sol: Complete Comparison and Report on Intro Pricing, Token Use, Cost per Task, and Cyber Benchmarks

Gemini 4 Carbon is the long-horizon frontier model that Google DeepMind announced on October 28, 2026 for a restricted group of cyber defenders and then opened on November 10, 2026 to paid-tier developers in the Gemini API, to Gemini for business customers and to Google AI Ultra subscribers. GPT-6.1 Sol is the OpenAI model whose system card addendum is dated September 29, 2026, the same day it became generally available in GitHub Copilot, and which OpenAI positions as near-Astra capability at a lower price in the API, Codex and ChatGPT Work. Both models accept up to about one million tokens of context, both bill reasoning tokens as output, and for the next few weeks both carry the same API list price.

The verdict depends on the date and on the workload. Until November 30, 2026 the two models cost the same per token, $2 per 1M input tokens and $10 per 1M output tokens, with cached input at $0.10. From December 1 Carbon moves to $4 input and $20 output, twice Sol's rate. On Artificial Analysis measurements Carbon at high thinking leads Sol at maximum effort on the Intelligence Index v5.0.1 (60 vs 55), on Terminal-Bench 4.0 (65.2% vs 56.1%), on AutomationBench-AA (77.8% vs 64.9%) and on HLE-Diamond (69.1% vs 58.8%), and it returns its first answer token sooner. Sol ties Carbon on FrontierCode 2.0 at 40.6%, edges it on GDP.pdf and CritPt, and costs far less to run because it writes fewer tokens: Artificial Analysis puts the average Intelligence Index task at $7.76 for Carbon and $1.27 for Sol, a 6.1x gap at Carbon's post-introductory rates.

Carbon is the stronger pick for long-running engineering agents, terminal work, scientific reasoning and defensive security teams that can justify a higher bill, and it is unusually good value during the introductory window. Sol is the better default for high-volume API traffic, cost-capped agents, ChatGPT Plus users and teams that need OpenAI's tool stack or Amazon Bedrock. All prices in this comparison were checked on the official Google Cloud, Gemini and OpenAI pages on November 12, 2026.

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GEMINI 4 CARBON AND GPT-6.1 SOL: RELEASE TIMELINE AND MODEL IDENTITY.

Google built Carbon on top of Gemini 4 Argon, which its model card names as the base model, and describes it as its strongest model for long-horizon agentic workflows across knowledge work, engineering, cybersecurity and scientific reasoning. The first release on October 28 was limited to a subset of cyber defenders and partners in the Fairwind Program. The November 10 release at 16:00 UTC is the broad release for most buyers, because it put the public `gemini-4-carbon` model on the paid tier of the Gemini API, in Google AI Studio and Android Studio, in Gemini for business, and in the Gemini app, AI Mode and Google Antigravity for AI Ultra subscribers. A separate `gemini-4-carbon-cyber-permissive` variant stays behind an allowlist.

OpenAI describes GPT-6.1 Sol as the newest model in the GPT-6 series and says it delivers capabilities comparable to GPT-6 Astra, its most powerful model, with an emphasis on speed and cost. Its direct predecessor is GPT-6 Sol. OpenAI's developer documentation lists a single identifier, `gpt-6.1-sol`, with no dated snapshot shown, and a knowledge cutoff of April 30, 2026, one month later than Carbon's March 2026 cutoff.

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Item	Gemini 4 Carbon	GPT-6.1 Sol
Developer	Google DeepMind	OpenAI
API model ID	gemini-4-carbon	gpt-6.1-sol
First release	Oct 28, 2026 (cyber defenders only)	Sep 29, 2026 (system card date)
Broad availability	Nov 10, 2026	Sep 29, 2026
Base or predecessor	Built on Gemini 4 Argon	Succeeds GPT-6 Sol
Knowledge cutoff	March 2026	April 30, 2026
Input modalities	Text, image, audio, video	Text, image
Output	Text	Text
Open weights	No	No

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The modality row is a real difference. Carbon accepts audio and video directly, up to roughly 45 minutes of video with audio or about 8.4 hours of audio per prompt on Google Cloud, while Sol's model page lists text and image input only, with audio and video marked as unsupported for the model itself.

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GEMINI 4 CARBON VS GPT-6.1 SOL API PRICING BEFORE AND AFTER NOVEMBER 30.

Google's Agent Platform pricing page lists Carbon at $2 input, $0.10 cached input and $10 output per 1M tokens for the global endpoint through November 30, 2026, and at $4, $0.20 and $20 from December 1. On Google Cloud the introductory rate is delivered as 50% credits back on net spend, and regional endpoints cost 10% more than global ones. Google's November 10 announcement states the same $2/$10 introductory price for the public Carbon model in the Gemini API and the same $4/$20 rate after the introductory period ends on November 30.

OpenAI lists GPT-6.1 Sol at $2 input, $0.10 cached input, $2.50 for cache writes and $10 output per 1M tokens. Sol carries a long-context surcharge that Carbon's Gemini 4 price table does not show: when a prompt exceeds 272K input tokens, the whole request is billed at twice the input and cache rates and 1.5 times the output rate. Batch and Flex processing cost 50% less than Standard, Fast mode costs twice Standard, Ultrafast costs six times Standard, and regional processing adds 10%.

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Price per 1M tokens (USD)	Carbon through Nov 30	Carbon from Dec 1	GPT-6.1 Sol
Input	$2.00	$4.00	$2.00
Cached input	$0.10	$0.20	$0.10
Cache write	No charge listed (implicit caching)	No charge listed (implicit caching)	$2.50
Output, including reasoning	$10.00	$20.00	$10.00
Input above 272K tokens	Same rate (no Gemini 4 tier shown)	Same rate (no Gemini 4 tier shown)	$4.00
Output when input above 272K	Same rate	Same rate	$15.00
Batch input / output	$1.00 / $5.00	$2.00 / $10.00	50% off Standard
Priority or Fast input / output	$3.60 / $18.00	$7.20 / $36.00	2x Standard (Fast)
Regional endpoint premium	+10% non-global	+10% non-global	+10% regional processing

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Two billing details change real costs. Google's pricing page says that from November 13, 2026, a Gemini 4 completion that runs past 32K tokens is charged for the retrievable tokens even if the request then fails on a client or server timeout, which affects long agent runs. OpenAI's cache-write charge means Sol's first pass over a long system prompt costs 25% more than ordinary input before the cheaper cached rate applies on later calls. Our Gemini API pricing guide tracks the full Gemini 4 and Gemini 3.8 price list.

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CONTEXT WINDOW, OUTPUT LIMITS AND THINKING CONTROLS IN CARBON AND SOL.

Carbon uses a unified budget of 1,048,576 tokens that covers input and output together, including thinking tokens, function calls and the final response. Without a `max_output_tokens` value the default output budget is 65,536 tokens. A single streaming segment produces up to 65,536 tokens, a non-streaming segment up to 32,768, and the long-decode mode on the `v1beta1` API can continue output up to the full window minus the input. Sol has a 1,050,000-token context window with a separate maximum of 128,000 output tokens, and in Codex and ChatGPT Work it runs with a default context of 500,000 tokens.

The reasoning controls differ in granularity. Carbon exposes three thinking levels, LOW, MEDIUM and HIGH, with MEDIUM as the default on the public model and HIGH on the cyber variant. Google's guide recommends HIGH for multi-file engineering, formal proofs and vulnerability discovery, and LOW for classification, summarization and extraction. Setting a numeric `thinking_budget` or the MINIMAL level returns an HTTP 400 error, and Carbon ignores temperature, top-p and top-k values. Sol offers five effort levels, from low through medium (the default), high and xhigh to max.

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Limit or control	Gemini 4 Carbon	GPT-6.1 Sol
Context window	1,048,576 tokens, input and output combined	1,050,000 tokens
Maximum output	Default budget 65,536; long decode up to window minus input	128,000 tokens
Default in agent products	Not stated	500K context in Codex and ChatGPT Work
Reasoning levels	LOW, MEDIUM, HIGH	low, medium, high, xhigh, max
Default level	MEDIUM (HIGH on cyber variant)	medium
Sampling parameters	Temperature, top-p, top-k ignored	Not stated on model page
Prompt caching	Implicit only, 4,096-token minimum prefix	Cached input and paid cache writes
Fine-tuning	Not supported	Not supported

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The tool sets overlap on the essentials. Carbon supports function calling, code execution, URL context, computer use and grounding with Google Search, Google Maps and several third-party web search providers, but it does not support the Gemini Live API or explicit context caching. Sol supports web search, file search, image generation, code interpreter, a hosted shell, apply patch, skills, computer use, MCP and tool search, and OpenAI's model page notes that tool calling requires the Responses API.

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WHERE EACH MODEL IS AVAILABLE: GEMINI APP, CHATGPT, API, CLOUD AND GITHUB COPILOT.

Access is the second big difference after price. Carbon is open to any developer on the paid tier of the Gemini API, but on Google Cloud's Agent Platform the model page describes it as available to verified enterprise customers in select regions, with `global`, `us` and `eu` locations. In Google's consumer products Carbon is limited to Google AI Ultra, which costs $99.99 a month for the 5x usage tier and $199.99 for the 20x tier in the United States, and Google's subscription page lists the highest Carbon access under Ultra.

Sol is broader on the OpenAI side. The ChatGPT plans page lists GPT-6.1 Sol in Plus and as expanded access in Pro, while Free and Go do not include it. OpenAI's documentation lists it in Codex (desktop app, CLI and IDE extension), ChatGPT Work, ChatGPT Credits and the API, on Amazon Bedrock as `openai.gpt-6.1-sol`, and on Google Cloud as a private preview. Our ChatGPT Plus vs Pro comparison covers the plan limits, and our report on OpenAI's GPT-6 models in Google Cloud Model Garden covers the Google Cloud route.

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Channel	Gemini 4 Carbon	GPT-6.1 Sol
Direct API	Gemini API paid tier (AI Studio, Android Studio)	OpenAI API (Build, Launch, Grow tiers)
Free API tier	No (paid tier only)	Not supported
Consumer app	Gemini app and AI Mode, AI Ultra only	ChatGPT Plus; expanded on Pro; not Free or Go
Coding agents	Google Antigravity (AI Ultra)	Codex desktop, CLI and IDE extension
Enterprise	Gemini for business; Agent Platform for verified customers	ChatGPT Work and Business/Enterprise per OpenAI docs
Other clouds	Google Cloud only	Amazon Bedrock; Google Cloud private preview
GitHub Copilot	GA since Nov 10 on Pro+, Max, Business, Enterprise	GA since Sep 29 on Pro+, Max, Business, Enterprise
Copilot billing	Provider list pricing (usage-based)	$2 / $10, long context $4 / $15 per 1M

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OpenAI's published API rate limits for Sol are 5,000 requests and 1M tokens per minute on the Build tier, 10,000 requests and 4M tokens on Launch, and 15,000 requests and 40M tokens on Grow. Google does not print per-model Carbon limits on its rate-limits page and points developers to AI Studio for them, and Carbon on Agent Platform does not support fixed quota.

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HOW THE CARBON AND SOL BENCHMARK FIGURES WERE PRODUCED.

The two vendors published different evidence. Google's Carbon model card compares Carbon with Gemini 4 Argon, GPT-6 Astra, Claude Opus 5.5 and Claude Fable 5.1, with results as of October 2026, and it does not include GPT-6.1 Sol. OpenAI's Sol system card addendum reports safety, health and Preparedness evaluations against GPT-6 Sol and GPT-6 Astra, and it contains no general coding or computer-use scores such as SWE-style or OSWorld results. OpenAI's own launch post was not reachable during this check, so its capability charts are not used here.

The only like-for-like measurements of both models come from Artificial Analysis, which runs the same nine evaluations on every model and publishes cost, token and latency data from each vendor's own API. Its comparison page pairs Carbon at high thinking with Sol at maximum effort, which is a fair top-setting match because HIGH is Carbon's highest public level and max is Sol's. Two cybersecurity benchmarks, SEC-bench Pro and ExploitGym, appear in both vendors' documents, but the runs differ in setup: Google says its cyber results reflect access through the Fairwind Program, and OpenAI reports ExploitGym as intended-vulnerability success per attempt, so those pairs are indicative and should not be read as a single leaderboard.

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ARTIFICIAL ANALYSIS HEAD-TO-HEAD: CARBON HIGH VS SOL MAX.

On Artificial Analysis Intelligence Index v5.0.1, Carbon at high thinking scores 60 and Sol at maximum effort scores 55. Artificial Analysis named Carbon the new number one on Index v5 on October 28 with a score of 59 on the earlier v5.0 version. The five-point gap comes mostly from agentic and knowledge evaluations: Carbon leads by 12.9 points on AutomationBench-AA, 10.3 points on HLE-Diamond, 9.1 points on Terminal-Bench 4.0 and 12 points on AA-Omniscience.

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Artificial Analysis evaluation	Gemini 4 Carbon (High)	GPT-6.1 Sol (Max)	Leader
Intelligence Index v5.0.1	60	55	Carbon +5
AA-Briefcase v1.1 (Elo)	1611	1555	Carbon +56
AutomationBench-AA	77.8%	64.9%	Carbon +12.9
Terminal-Bench 4.0	65.2%	56.1%	Carbon +9.1
FrontierCode 2.0	40.6%	40.6%	Tie
AA-Omniscience v1.1	54	42	Carbon +12
GDP.pdf	28.4%	31.0%	Sol +2.6
HLE-Diamond	69.1%	58.8%	Carbon +10.3
CritPt v2 (Hard Subset)	69.0%	69.5%	Sol +0.5
Terminal-Bench-Science 0.1	64.8%	58.1%	Carbon +6.7

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Sol's wins are narrow. It leads by 2.6 points on GDP.pdf and by half a point on the hard subset of CritPt v2, and it matches Carbon exactly on FrontierCode 2.0. The Omniscience result also carries a reliability signal: in its October 28 analysis Artificial Analysis measured Carbon's hallucination rate on AA-Omniscience at 11%, the second lowest it had recorded, against 50% for both Claude Opus 5.5 and GPT-6 Astra at maximum effort. The comparison page does not publish Sol's hallucination rate, and OpenAI's system card describes Sol's hallucination results only qualitatively.

Effort setting changes Sol's profile sharply. At low effort Artificial Analysis scores Sol at 35 on the Index, with 30.8% on Terminal-Bench 4.0, at a cost of $0.16 per task and a first token in 2.84 seconds. Buyers who run Sol below max should expect results much closer to that low-effort profile than to the 55-point headline.

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CYBERSECURITY SCORES FROM GOOGLE'S MODEL CARD AND OPENAI'S SYSTEM CARD.

Cybersecurity is the domain Google chose to lead with, and the vendor numbers favor Carbon on both shared benchmarks. Google's model card gives Carbon 88.2% on SEC-bench Pro and 54.8% on ExploitGym. OpenAI's system card gives Sol 78.8% pass@1 on SEC-Bench Pro and 35.1% intended-vulnerability success per attempt on ExploitGym, and it reports GPT-6 Astra at 85.4% and 42.4% on the same two tests. Google's card lists GPT-6 Astra at exactly those 85.4% and 42.4% figures, which suggests the two companies are reporting the same benchmark versions, although the test setups and effort settings are not fully disclosed on either side.

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Cyber benchmark	Gemini 4 Carbon	GPT-6.1 Sol	Source and settings
SEC-bench Pro	88.2%	78.8% pass@1	Google model card (Fairwind access); OpenAI system card (effort not stated)
ExploitGym	54.8%	35.1% per attempt	Google model card; OpenAI system card (intended vulnerability)
CWE-bench (patching)	71.0%	Not published	Google model card
ExploitBench	Not published	99.7% at max effort	OpenAI system card, contamination caveat
ExploitBench Internal Port	Not published	21.5%	OpenAI system card (June to August 2026 set)
GPT-6 Astra reference	85.4% / 42.4%	85.4% / 42.4%	Both cards, SEC-bench Pro / ExploitGym

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The practical reading is limited by access rules. Carbon's cyber figures reflect access through the Fairwind Program, which Google uses for the allowlisted `gemini-4-carbon-cyber-permissive` variant, so a general API customer on the public model may see more refusals on offensive tasks than the card's setting implies. OpenAI routes Sol's cyber access through a phased Daybreak program for trusted defenders. OpenAI itself flags that Sol's 99.7% ExploitBench score may be inflated by contamination from historical vulnerabilities, which is why the internal-port result of 21.5% is the more conservative figure.

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BENCHMARKS ONLY GOOGLE OR ONLY OPENAI PUBLISHED.

Most of Carbon's model card has no Sol counterpart. Google reports Carbon at 61.2% on FrontierSWE 2, 81.5% on the offline subset of OSWorld-2.1, 53.9% on Zapier's AutomationBench, a $16,484 bank balance on Vending-Bench 2 and 96.2% F1 on GraphWalks between 256K and 1M tokens. On two of those rows Carbon trails other models in Google's own table: GPT-6 Astra scores 65.5% on FrontierSWE 2 and Claude Opus 5.5 scores 87.2% on OSWorld-2.1.

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Benchmark (vendor-only)	Gemini 4 Carbon	GPT-6.1 Sol	Published by
FrontierSWE 2	61.2%	Not published	Google
OSWorld-2.1 (offline subset)	81.5%	Not published	Google
AutomationBench (Zapier)	53.9%	Not published	Google
Vending-Bench 2 (bank balance)	$16,484	Not published	Google
GraphWalks 256K to 1M (BFS F1)	96.2%	Not published	Google
Vals Index v2.2	70.4%	Not published	Google
HealthBench Professional	Not published	64.2	OpenAI
MentalHealthBench (max effort)	Not published	57.9	OpenAI
Internal Research Debugging	Not published	75.52%	OpenAI
Coding deception rate	Not published	1.50%	OpenAI

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OpenAI's Sol-only figures are mostly safety and health measures. Sol scores 64.2 on HealthBench Professional against 64.7 for GPT-6 Astra, 57.9 on MentalHealthBench at maximum effort, and 75.52% on OpenAI's internal research-debugging rubric, which OpenAI says remains below its High threshold for AI self-improvement. The alignment section reports a 1.50% misrepresentation rate on coding-deception tests, higher than Astra's 0.51%, and unwanted persistence after warnings in 23.5% of rollouts against 17.4% for Astra, a result OpenAI notes was measured without system-level controls.

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TOKEN CONSUMPTION, SPEED AND COST PER TASK FOR CARBON AND SOL.

Carbon's per-token price parity with Sol hides a large difference in how many tokens each model writes. Artificial Analysis counts 123,000 output tokens per Intelligence Index task for Carbon at high thinking against 67,800 for Sol at max effort, a ratio of 1.81, and 205 million output tokens to run the whole index against 63.7 million. Google's own announcement says Argon remains its recommended choice for most software engineering and knowledge work because Carbon uses more tokens, and the model card lists higher token use at higher thinking levels among Carbon's known limitations.

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Artificial Analysis measurement	Gemini 4 Carbon (High)	GPT-6.1 Sol (Max)
Output tokens per task	123k	67.8k
Reasoning tokens per task	72.2k	49.2k
Output tokens to run the index	205M	63.7M
Cost per task	$7.76	$1.27
Cost to run the index	$8,240	$1,124
Blended price (7:2:1 cache/input/output)	$2.94	$1.47
Output speed	82 tokens/s	56 tokens/s
Time to first answer token	67.54 s	153.38 s
End-to-end time, single request	157.33 s	369.71 s
Time per task	24.9 min	20.1 min

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Artificial Analysis prices Carbon at the standard $4/$20 rate, so its $7.76 per task already reflects the December price. At the introductory rate the same work would cost about $3.88, which still leaves Carbon roughly three times more expensive per task than Sol at maximum effort. On speed the picture is mixed. Carbon streams 46% faster and reaches its first answer token in less than half the time on single requests, and its mean end-to-end time is 2.35 times shorter, yet its average full index task takes 24.9 minutes against 20.1 for Sol because it writes more tokens.

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WHAT THREE TYPICAL WORKLOADS COST ON CARBON AND SOL.

The worked examples below use official list prices and stated assumptions, with the arithmetic run in Python. The coding agent task assumes 2 million input tokens accumulated across turns with 85% cache hits and 60,000 output tokens, first with identical token counts and then with Carbon's output scaled by the 1.81 ratio that Artificial Analysis measured. The assistant workload assumes 10,000 requests a day for 30 days, each with 3,000 input tokens (2,000 of them a cached shared prefix) and 500 output tokens. The long-document job is one request with a 600,000-token prompt and 8,000 output tokens. Sol's cache-write charge is left out of these totals because it depends on how often a prefix is rewritten.

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Workload	Carbon through Nov 30	Carbon from Dec 1	GPT-6.1 Sol	Dec 1 Carbon vs Sol
Coding agent task, same tokens	$1.37	$2.74	$1.37	2.00x
Coding agent task, Carbon writes 1.81x output	$1.86	$3.72	$1.37	2.71x
Assistant, 300,000 requests a month	$2,160	$4,320	$2,160	2.00x
Same assistant on Batch	$1,080	$2,160	$1,080	2.00x
One 600K-token document, 8K output	$1.28	$2.56	$2.52	1.02x

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The long-document row shows where Sol's surcharge bites. Because the prompt exceeds 272K tokens, Sol bills the whole request at $4 input and $15 output, which brings it level with Carbon's post-introductory price and makes Carbon half the cost during the introductory period. Google's Gemini 4 price table shows no separate long-context tier, and nothing in the published rates penalizes Carbon for a large prompt. For short and medium prompts the comparison is simple: identical cost until November 30, then twice the per-token price for Carbon, with the measured token gap pushing the real difference higher.

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SAFETY CLASSIFICATIONS, CYBER ACCESS AND RESTRICTIONS FOR CARBON AND SOL.

OpenAI treats GPT-6.1 Sol as Critical in cybersecurity and High in biological and chemical capability under its Preparedness Framework, and below the High threshold for AI self-improvement. Sol uses the same safeguards stack as GPT-6 Astra. In OpenAI's static jailbreak tests Sol's defender success rate is 85.8% on cyber prompts and 93.8% on high-risk biology prompts, lower than Astra's 91.5% and 97.3% on the same tests.

Google's Carbon model card points to a separate Safety and Security Report for the Frontier Safety Framework assessment and does not list critical capability levels on the card itself. Google's November 10 announcement reports that Carbon blocked 99% of attack trajectories from an adaptive red-teaming agent, with a per-attempt breach rate of 0.8% against 3.1% for Argon, and that Gray Swan's automated cyberattack queries succeeded 2.17% of the time against 9.86% for Argon. Trajectory Labs found no new universal jailbreaks across 132 hours and more than 13,000 requests, and Carbon passed 92% of a 375-task agentic honesty benchmark against 70% for Argon. Google also writes that resistance to prompt injection remains an area for continued improvement, and the model card lists hallucinations and occasional slowness or timeouts as known limitations.

For enterprise buyers the contract terms differ as well. Carbon is classed as a Google Frontier Model under Section 34 of Google Cloud's Service Specific Terms and supports data residency, customer-managed encryption keys and VPC Service Controls on Agent Platform. Sol's model page lists a 10% premium for regional processing where available. Our report on the November 10 Carbon release covers the access rules for the app and business channels.

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GAPS IN THE PUBLISHED EVIDENCE FOR CARBON AND SOL.

Several figures a buyer would want are missing. Google has not published per-model Carbon rate limits on its rate-limits page, and GitHub's Copilot price table does not yet list Carbon, even though the changelog says it is billed at provider list pricing. OpenAI's capability claims for Sol on coding and computer-use benchmarks were not reachable on an official page during this check, and its system card does not state the effort setting behind most of its cyber and health scores. Neither vendor has published a direct Carbon-versus-Sol evaluation, so the Artificial Analysis comparison is the only source that tests both models the same way.

The Gemini 4 price table on Google Cloud shows one rate for every prompt size, and Google's footnotes about a 200K-token threshold appear only under the Gemini 3 and 2.5 sections, so the absence of a long-context surcharge for Carbon is read from the table rather than stated in a sentence. Carbon's plan inclusion for Google AI Pro is ambiguous on the subscription page, while Google's announcement limits Gemini app and AI Mode access to AI Ultra subscribers.

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CHOOSING BETWEEN GEMINI 4 CARBON AND GPT-6.1 SOL BY WORKLOAD.

The measurements point in a consistent direction. Carbon buys higher scores on agentic, terminal and science evaluations at the cost of more tokens, and from December it also charges twice as much per token. Sol delivers most of that capability at a much lower cost per task, with better distribution across ChatGPT, Codex, Bedrock and Copilot.

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Workload or buyer	Recommended	Reason
Long-running engineering agents where quality decides	Gemini 4 Carbon	Terminal-Bench 4.0 65.2% vs 56.1%; AutomationBench-AA 77.8% vs 64.9%
High-volume chat, support or extraction	GPT-6.1 Sol	Same price to Nov 30, half Carbon's price after; fewer output tokens
Very long prompts above 272K tokens	Gemini 4 Carbon	No long-context surcharge shown; Sol bills $4 / $15 above 272K
Audio and video understanding	Gemini 4 Carbon	Native audio and video input; Sol takes text and image
Cost-capped agent fleets	GPT-6.1 Sol	$1.27 vs $7.76 per Artificial Analysis task
Defensive security research	Gemini 4 Carbon	SEC-bench Pro 88.2% vs 78.8%; ExploitGym 54.8% vs 35.1% (vendor runs)
ChatGPT Plus or Codex users	GPT-6.1 Sol	Included in Plus and Codex; Carbon needs AI Ultra from $99.99
AWS-based deployments	GPT-6.1 Sol	Available on Amazon Bedrock; Carbon is Google Cloud only
Scientific reasoning and knowledge recall	Gemini 4 Carbon	HLE-Diamond 69.1% vs 58.8%; AA-Omniscience 54 vs 42

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For a team that can move work before November 30, Carbon at $2/$10 is the stronger model for the same per-token price, and the main cost to watch is its higher output volume. After December 1, Sol at maximum effort becomes the efficient default for most API traffic, and Carbon earns its premium on long-horizon agents, multimodal inputs, very long prompts and security work where its measured lead on Terminal-Bench, AutomationBench and the cyber benchmarks is worth roughly twice the per-token bill.

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