Gemini Argon Price Doubles Nov. 30, Sonnet 5.5 Holds [2026]

For 45 days, three of the industry’s newest flagship language models are sitting at nearly identical prices. Starting December 1, 2026, that changes. Google’s Gemini 4 Argon, which launched October 13 at an introductory $2 per million input tokens and $10 per million output tokens, jumps to a standard $4/$20 per million once the promotional window closes on November 30, 2026, according to Google Cloud’s own pricing announcement. Meanwhile, Anthropic’s Claude Sonnet 5.5 (released September 27) and OpenAI’s GPT-6.1 Sol (released September 22) are holding at $2/$10 per million tokens with no expiration date attached.

That pricing cliff is only half the story. On the Artificial Analysis Intelligence Index, tracked publicly by both TechTimes and ModelCap, Claude Sonnet 5.5 is currently outscoring both GPT-6.1 Sol and Gemini 4 Argon on raw reasoning, even though it costs exactly the same as GPT-6.1 Sol today and less than Gemini 4 Argon will cost from December onward. This comparison breaks down the specs, the benchmark data from four independent leaderboards, the pricing math your finance team needs before the Argon deadline, and which model actually fits which workload, using only officially published numbers and live leaderboard snapshots dated October 13-16, 2026.

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Why This Three-Way Comparison Matters Right Now

All three models shipped within a three-week window this fall: GPT-6.1 Sol on September 22, Claude Sonnet 5.5 on September 27, and Gemini 4 Argon on October 13. That clustering is not a coincidence. Each is positioned by its maker as the “efficient frontier” tier, the model you reach for when you need near-flagship reasoning without paying flagship prices for a model like Claude Opus 5.5 or GPT-6 Astra. A Yahoo Finance analysis of the Frontier Model Index found that the gap between the top cluster of models, including Claude Sonnet 5.5 (Max) and Muse Wonder Full (Max), had compressed to just 2 points out of 127 models evaluated, a sign that raw intelligence scores are converging across vendors and that pricing and ecosystem fit are becoming the real differentiators.

That convergence is exactly why the Gemini 4 Argon pricing deadline matters so much. When three models deliver comparable output quality, the one whose price doubles in six weeks stops being the obvious default. Teams building on any of these three models need to understand not just who wins on a given benchmark today, but what their monthly API bill looks like on December 1.

It also matters because all three vendors are explicitly marketing these models as the practical, everyday option, not the showcase flagship. OpenAI still sells the pricier GPT-6 Astra, Anthropic still sells Claude Opus 5.5, and Google has its own higher tiers above Argon. GPT-6.1 Sol, Claude Sonnet 5.5, and Gemini 4 Argon are the models most engineering teams will actually run in production because the flagship tier is usually priced for occasional, high-stakes queries rather than sustained API traffic. That makes this specific three-way comparison, rather than a flagship-versus-flagship match-up, the one that determines actual monthly infrastructure spend for most companies building AI features today.

GPT-6.1 Sol vs Claude Sonnet 5.5 vs Gemini 4 Argon: Full Specs Comparison

Here is the complete specifications breakdown pulled directly from each vendor’s own model documentation and launch pages, current as of October 16, 2026.

Spec	GPT-6.1 Sol	Claude Sonnet 5.5	Gemini 4 Argon
Developer	OpenAI	Anthropic	Google DeepMind
Release date	Sept. 22, 2026	Sept. 27, 2026	Oct. 13, 2026
Context window	1,050,000 tokens	1,000,000 tokens	Not publicly disclosed in launch docs
Max output tokens	128,000	128,000 (300K in Batch API beta)	Not publicly disclosed in launch docs
Input price (current)	$2.00 / million tokens	$2.00 / million tokens	$2.00 / million tokens (intro, through Nov. 30)
Output price (current)	$10.00 / million tokens	$10.00 / million tokens	$10.00 / million tokens (intro, through Nov. 30)
Price after Dec. 1, 2026	No change announced	No change announced	$4.00 / $20.00 per million tokens
Cached input price	$0.10 / million tokens	$0.10 / million tokens (cache read)	95% discount through Nov. 30
Reasoning / thinking mode	Yes, reasoning.effort parameter	Yes, adaptive thinking	Not detailed in pricing docs
Effort / default setting	low/medium/high/xhigh/max (default: medium)	Adaptive (default: high)	Not specified
Knowledge cutoff	April 30, 2026	June 2026	Not specified in sources reviewed
Headline benchmark claim	DeepSWE v1.1, AutomationBench, OSWorld 2.0 gains cited	Fewer tokens than Sonnet 5 for same tasks	#1 on Vals Index (finance, coding, legal, tax)

The most immediately useful line in that table is the pricing row. GPT-6.1 Sol and Claude Sonnet 5.5 are, as of today, priced identically: $2 per million input tokens and $10 per million output tokens. Gemini 4 Argon matches that figure only temporarily. Google’s own Cloud blog post is explicit that the $2/$10 rate is an “introductory” rate that expires November 30, 2026, after which standard pricing of $4 input / $20 output takes over, doubling the cost of every Argon token processed from that date forward.

Context window is the other notable gap. GPT-6.1 Sol’s 1,050,000-token window is the largest of the three by a slim margin over Claude Sonnet 5.5’s 1,000,000 tokens. Google has not published an equivalent figure for Gemini 4 Argon in the sources reviewed for this piece, which makes direct document-processing comparisons harder until Google publishes full technical specs.

How This Efficiency Tier Stacks Up Against the Pricier Flagships

None of these three models is the most capable thing its maker sells. OpenAI, Anthropic, and Google all keep a pricier flagship above this tier, and the gap between tiers is worth quantifying before anyone assumes the cheaper option is leaving serious performance on the table. On BenchLeader’s October 15 leaderboard, Claude Opus 5.5 topped the rankings at 71.6, with Gemini 4 Argon close behind at 71.1. On the TechTimes AA Intelligence Index snapshot published October 16, Claude Opus 5.5 again led at 57.6, followed by Claude Sonnet 5.5 at 56.0, GPT-6 Astra at 52.7, Gemini 4 Argon at 52.6, and GPT-6.1 Sol at 51.8.

What that ordering shows is that Claude Sonnet 5.5 sits closer to its own flagship sibling, Claude Opus 5.5, than GPT-6.1 Sol sits to GPT-6 Astra, at least on this specific index. A 1.6-point gap separates Opus 5.5 and Sonnet 5.5 on the AA Intelligence Index, while GPT-6 Astra beats GPT-6.1 Sol by nearly a full point despite GPT-6.1 Sol being the more recently released of the two OpenAI models. On Humanity’s Last Exam, Claude Opus 5.5’s 61.4% comfortably clears Gemini 4 Argon’s 57.1%, Claude Sonnet 5.5’s 55.0%, and GPT-6.1 Sol’s 52.9%, confirming that for the hardest, most open-ended reasoning questions, paying for the flagship tier still buys a measurable gain. For teams deciding whether to upgrade to Opus 5.5 or GPT-6 Astra instead of staying on the efficiency tier, that 6-to-9-point Humanity’s Last Exam gap is the clearest published justification for the extra spend; for everyday coding and agentic work, the gap closes substantially or disappears.

Benchmark Results From Four Independent Leaderboards

No single benchmark tells the whole story, so this comparison pulls scores from four separately maintained leaderboards: BenchLeader, BenchLM, TechTimes’ AI Model Benchmarks page, and ModelCap’s Arena rankings. Scores were captured October 15-16, 2026.

Benchmark / Leaderboard	GPT-6.1 Sol	Claude Sonnet 5.5	Gemini 4 Argon
AA Intelligence Index (TechTimes, Oct. 16)	51.8	56.0	52.6
Terminal-Bench 4.0 (agentic CLI tasks)	Not published in sources reviewed	59.9%	57.6%
Humanity’s Last Exam (raw reasoning)	52.9%	55.0%	57.1%
Arena text leaderboard rank (ModelCap, 213 models)	Not top-ranked in snapshot	Not top-ranked in snapshot	#1
Arena coding leaderboard rank (ModelCap, 213 models)	Not top-ranked in snapshot	Not top-ranked in snapshot	#1 (rating 1559)
BenchLM 90% confidence-interval tie status (Oct. 15)	Tied with Claude Opus 5.5	Not in top tie group	Not in top tie group

Two patterns stand out. First, no model wins every benchmark. Claude Sonnet 5.5 leads on the AA Intelligence Index and Terminal-Bench 4.0, Gemini 4 Argon leads on Humanity’s Last Exam and both ModelCap Arena leaderboards, and GPT-6.1 Sol’s standout result is a statistical tie with the considerably more expensive Claude Opus 5.5 on BenchLM’s October 15 snapshot. Second, the AA Intelligence Index numbers directly undercut the idea that higher price buys higher reasoning: Claude Sonnet 5.5 at $2/$10 per million tokens outscores GPT-6.1 Sol at the identical price point and Gemini 4 Argon at its pre-December price point.

Terminal-Bench 4.0 and Agentic Coding

Terminal-Bench 4.0 measures how well a model can operate inside a real command-line environment, running shell commands and completing multi-step tasks end to end rather than just generating a code snippet. On this measure, Claude Sonnet 5.5’s 59.9% is the strongest of the three figures captured in this comparison, ahead of Gemini 4 Argon’s 57.6%. GPT-6.1 Sol’s launch materials cite gains on DeepSWE v1.1, AutomationBench, and OSWorld 2.0 instead of Terminal-Bench 4.0, so a direct score was not available in the sources reviewed for this article. For teams building terminal-driven coding agents specifically, the available data favors Sonnet 5.5.

Humanity’s Last Exam and Raw Reasoning

Humanity’s Last Exam is designed to be difficult even for frontier models, pulling from expert-level questions across dozens of academic disciplines. Here the ranking flips: Gemini 4 Argon’s 57.1% leads, with Claude Sonnet 5.5 at 55.0% and GPT-6.1 Sol at 52.9% trailing. This is the clearest evidence in the available data that Gemini 4 Argon’s reasoning strength shows up more in open-ended, expert-level questions than in agentic coding tasks, where Sonnet 5.5 has the edge.

Pricing Breakdown: Why Gemini 4 Argon’s Clock Is Ticking

Pricing is where this comparison becomes a planning problem rather than just a benchmark exercise. Here is the full breakdown, including cache pricing, which matters enormously for any team running repeated prompts against the same context.

Pricing Item	GPT-6.1 Sol	Claude Sonnet 5.5	Gemini 4 Argon
Input, per million tokens	$2.00	$2.00	$2.00 through Nov. 30; $4.00 from Dec. 1
Output, per million tokens	$10.00	$10.00	$10.00 through Nov. 30; $20.00 from Dec. 1
Cached input, per million tokens	$0.10	$0.10 (cache read)	95% discount through Nov. 30
5-minute cache write	Not published separately	$2.50	Not published
1-hour cache write	Not published separately	$4.00	Not published
Savings vs. predecessor model	50% cheaper cached input than GPT-6 Sol	Cache reads half of Sonnet 5’s $0.20	N/A, new model line
Monthly cost, 50M input / 10M output tokens*	$200	$200	$200 now; $400 from Dec. 1

*Illustrative calculation based on published per-million-token rates; actual costs depend on caching behavior, prompt length, and reasoning effort settings, which all three vendors confirm affect total token consumption.

Run that math for a team processing 50 million input tokens and 10 million output tokens a month, a realistic load for a mid-sized engineering org running an AI coding assistant across dozens of developers. At today’s rates, all three models cost roughly $200 a month for that volume. On December 1, the same workload on Gemini 4 Argon jumps to roughly $400, while GPT-6.1 Sol and Claude Sonnet 5.5 stay flat at $200, assuming neither vendor changes pricing between now and then, which is never guaranteed in a market moving this fast.

Cache pricing deserves its own line item because it compounds at scale. Claude Sonnet 5.5’s cache read price of $0.10 per million tokens is half of predecessor Claude Sonnet 5’s $0.20 rate, according to Anthropic’s own pricing documentation. GPT-6.1 Sol’s cached input similarly undercuts its own predecessor, GPT-6 Sol, by roughly 50%, per OpenAI’s launch post. Any team running repeated system prompts, long documents, or multi-turn agent sessions should model cache hit rates before comparing sticker prices, since a model with a higher base price but better caching can end up cheaper in production.

Scaling that same math across three different usage tiers makes the December cliff easier to plan around, whether you are running a small side project or a company-wide rollout.

Monthly Volume (input/output tokens)	GPT-6.1 Sol	Claude Sonnet 5.5	Gemini 4 Argon (now)	Gemini 4 Argon (Dec. 1+)
5M / 1M (small project)	$20	$20	$20	$40
50M / 10M (mid-sized team)	$200	$200	$200	$400
500M / 100M (company-wide rollout)	$2,000	$2,000	$2,000	$4,000

At every volume tier, the arithmetic is the same: a company-wide rollout on Gemini 4 Argon that costs $2,000 a month today costs $4,000 a month starting December 1, an extra $24,000 a year, without a single additional token processed. That is the number worth bringing to a budget meeting before signing a long-term commitment to Argon over the other two models, both of which have no equivalent increase scheduled as of this writing.

Context Windows and Output Limits: Does Size Matter Here

GPT-6.1 Sol’s 1,050,000-token context window edges out Claude Sonnet 5.5’s 1,000,000 tokens, though the practical difference between 1.05 million and 1 million tokens is marginal for almost any real document or codebase. Both cap maximum output at 128,000 tokens per response, with Claude Sonnet 5.5 extending to 300,000 tokens in its Batch API beta for asynchronous, non-interactive jobs. Google has not published an equivalent context-window figure for Gemini 4 Argon in the launch and pricing material reviewed for this comparison, which is itself worth noting: buyers evaluating Argon for large-document or large-codebase workloads should request this spec directly from Google Cloud before committing, rather than assuming parity with the other two models.

For most coding and agentic use cases, the 128,000-token output ceiling matters more than the input context size. A model that can read a million-token codebase but only write 128,000 tokens back still needs to work in chunks for very large refactors or multi-file generation tasks. All three vendors solve this the same way: encourage iterative, multi-call workflows rather than single giant generations, and that holds true across GPT-6.1 Sol, Claude Sonnet 5.5, and Gemini 4 Argon alike.

Developer Experience: How the APIs Actually Differ

Beyond raw benchmark scores, the day-to-day experience of building against these three APIs differs in ways that show up in production code, not just in a comparison table. GPT-6.1 Sol exposes a single reasoning.effort parameter with five discrete levels: low, medium, high, xhigh, and max, with medium set as the default according to OpenAI’s own API documentation. That gives developers an explicit dial to trade latency and cost against answer quality on a per-request basis, which is useful for applications that need to serve both quick, cheap lookups and slower, harder reasoning tasks from the same codebase.

Claude Sonnet 5.5 takes a different approach with what Anthropic calls adaptive thinking, defaulting to a “high” effort setting out of the box. Rather than forcing developers to pick an effort level upfront for every call, the model is designed to allocate its own reasoning budget based on the apparent difficulty of the prompt. Anthropic’s own documentation frames this as part of why Sonnet 5.5 needs fewer total tokens than its predecessor to complete comparable tasks, since the model is not spending a fixed reasoning budget on easy requests that do not need it.

Gemini 4 Argon’s equivalent reasoning controls were not detailed in the Google Cloud and Google blog posts reviewed for this comparison, which is a gap worth flagging for any team doing a side-by-side technical evaluation rather than a pure benchmark comparison. Teams planning to build latency-sensitive applications on Argon should confirm directly with Google Cloud’s documentation whether an equivalent effort or thinking-budget control exists before committing to production use, since neither reviewed source described Argon’s internal reasoning behavior with the same level of detail OpenAI and Anthropic provided for their own models.

Coding and Agentic Workflows: Which Model Actually Ships Code

For teams specifically evaluating these three for coding assistants, code review bots, or autonomous coding agents, Terminal-Bench 4.0 is the most relevant published number, since it tests multi-step, tool-using behavior inside an actual shell rather than isolated code generation. Claude Sonnet 5.5’s 59.9% on that benchmark is the strongest published figure among the three. OpenAI’s own GPT-6.1 Sol launch post leans on DeepSWE v1.1 and OSWorld 2.0 instead, both of which measure software-engineering and computer-use tasks, but without a directly comparable Terminal-Bench 4.0 number published at the time of writing, it is not possible to rank GPT-6.1 Sol against the other two on this specific test.

Gemini 4 Argon’s strongest coding-adjacent claim is its #1 position on both the Arena text and Arena coding leaderboards tracked by ModelCap across 213 models, with a coding rating of 1559. Arena-style leaderboards rely on aggregated user preference voting rather than a fixed test set, so they capture something closer to “which output do real users prefer” rather than pass/fail correctness on a benchmark suite. That is a meaningfully different signal than Terminal-Bench’s task-completion score, and the two should not be read as measuring the same thing.

Reasoning and Enterprise Tasks: Finance, Legal, and Tax

Gemini 4 Argon’s headline credential outside of coding is its #1 ranking on the Vals Index, a benchmark Google specifically cites as covering finance, legal, and tax workflows alongside coding. Google’s own launch post for Argon frames this as evidence the model outperforms other frontier models on benchmarks across domains, a claim tied directly to multi-step, document-heavy professional tasks rather than conversational chat. For regulated industries where a model needs to reason through tax code, contract language, or financial filings in sequence, this is the most directly relevant published credential among the three models in this comparison.

Claude Sonnet 5.5’s reasoning strength shows up differently: Anthropic’s own announcement emphasizes that the model needs far fewer tokens to do the same work as its predecessor, Claude Sonnet 5, which is a cost-efficiency claim as much as a capability claim. For enterprise teams running high-volume reasoning workloads where token consumption drives the bill, that efficiency gain can matter more than a few points on a leaderboard.

Real-World Use Cases: Five Scenarios Where Each Model Fits

Translating benchmark scores into day-to-day engineering decisions requires mapping each model’s measured strength to an actual workload. Based on the specs and benchmark data above, here are five scenarios where the choice between these three models is directly informed by the published numbers.

• A fintech engineering team running automated compliance code review on a CI/CD pipeline would lean toward Claude Sonnet 5.5, given its 59.9% Terminal-Bench 4.0 score and matching $2/$10 pricing with GPT-6.1 Sol, making it the stronger agentic-coding pick at no price premium.
• A tax-preparation SaaS company building a document-reasoning assistant has the clearest published signal pointing to Gemini 4 Argon, given its #1 Vals Index ranking, which Google explicitly ties to finance, legal, and tax reasoning tasks, as long as the team budgets for the December 1 price increase.
• An indie developer running a high-volume chatbot with repeated system prompts benefits most from aggressive prompt caching, making Claude Sonnet 5.5’s $0.10-per-million-token cache reads, half of its predecessor’s rate, the most cost-predictable option for sustained, repetitive workloads.
• An enterprise helpdesk automating multi-turn customer support tickets that needs the largest possible context window to ingest full customer histories would lean toward GPT-6.1 Sol’s 1,050,000-token window, the largest of the three published figures, for ingesting long ticket threads in a single call.
• A research team benchmarking models against expert-level academic questions for an internal evaluation suite would weight Gemini 4 Argon most heavily, given its 57.1% score on Humanity’s Last Exam, the strongest of the three published results on that specific test.
• A legal-tech startup building a contract-review assistant operating across finance, tax, and legal document types would also look first at Gemini 4 Argon, since the #1 Vals Index ranking spans exactly those three domains, though the team should price the December rate increase into its per-seat subscription model before launch.
• A platform team deciding whether to upgrade from the efficiency tier to a flagship model for its hardest internal reasoning tasks has a clear data point to weigh: Claude Opus 5.5’s 61.4% on Humanity’s Last Exam beats all three efficiency-tier models by 6 to 9 points, which may justify the added cost for a narrow set of high-stakes queries routed separately from routine traffic.

Use-Case Recommendations: Which Model Fits Your Team

Beyond the five scenarios above, here is a more direct set of recommendations by team type and constraint.

• Best for agentic, terminal-driven coding tools today: Claude Sonnet 5.5, based on its lead on Terminal-Bench 4.0 and the AA Intelligence Index at identical pricing to GPT-6.1 Sol.
• Best for regulated, document-heavy enterprise reasoning: Gemini 4 Argon, based on its #1 Vals Index ranking across finance, legal, and tax domains, provided the budget accounts for the Dec. 1 price doubling.
• Best for the largest single-call context ingestion: GPT-6.1 Sol, with the largest published context window of the three at 1,050,000 tokens.
• Best value before December 1, 2026: Gemini 4 Argon, which currently matches the other two on price while leading two ModelCap Arena leaderboards, but only for teams that can either lock in usage before the deadline or accept the doubled rate afterward.
• Best long-term, price-stable pick for budget planning: Claude Sonnet 5.5 or GPT-6.1 Sol, since neither vendor has announced an expiration date on current pricing, unlike Gemini 4 Argon’s explicit Nov. 30 cutoff.
• Best for teams already standardized on an ecosystem: whichever model matches your existing vendor, since all three require separate API integration, billing, and prompt-tuning work, and switching costs are real even when benchmark gaps are small.

Migration Guide: Moving Between These Three Models

Teams currently running GPT-6 Astra, Claude Opus 5.5, older Sonnet or Sol versions, or an open-weight model and considering a move to one of these three should plan for the following steps before flipping production traffic. Migrating between model vendors is rarely a one-line change in practice, even when the API surface looks similar on paper, because pricing structure, default reasoning behavior, and context-window ceilings all shift at the same time. Treat the migration as a staged rollout rather than a single cutover, and budget at least a week of parallel testing against your own evaluation set before retiring the old model entirely.

• Audit current token volume by splitting input and output separately, since all three models price input and output at different rates, and the ratio materially changes your total bill.
• Update the model identifier string in your API calls. OpenAI’s endpoint uses a dedicated gpt-6.1-sol model slug documented at OpenAI’s developer docs, while Anthropic and Google use their own model-string conventions.
• If migrating to GPT-6.1 Sol, explicitly set the reasoning.effort parameter (low, medium, high, xhigh, or max) rather than relying on defaults, since effort level directly changes both latency and output token count.
• If migrating to Claude Sonnet 5.5, review Anthropic’s adaptive thinking defaults, which ship at “high” effort out of the box according to Anthropic’s pricing documentation, and may need tuning down for latency-sensitive applications.
• Re-test prompt caching configuration from scratch. Cache write and read pricing differ meaningfully across all three vendors, and a cache strategy tuned for one model’s pricing structure will not automatically translate to another.
• If adopting Gemini 4 Argon, build a cost model for both the current $2/$10 rate and the post-Nov. 30 $4/$20 rate before committing, and flag the Dec. 1 transition date on your team’s billing calendar now.
• Re-run your existing evaluation suite rather than trusting public benchmark scores alone, since Terminal-Bench 4.0, Humanity’s Last Exam, and the AA Intelligence Index each rank these models differently, and your specific workload may resemble one benchmark far more than another.
• Check knowledge cutoff dates before migrating time-sensitive applications: GPT-6.1 Sol’s cutoff is April 30, 2026, while Claude Sonnet 5.5’s is June 2026, a two-month gap that can matter for apps referencing recent events or software versions.
• Budget for a parallel-run period where both the old and new model serve a portion of production traffic, since benchmark gains do not always translate directly into your specific prompt patterns.

A minimal code change for swapping model identifiers, using OpenAI’s API as an example, looks like this:

response = client.responses.create(
    model="gpt-6.1-sol",
    input=[{"role": "user", "content": prompt}],
    reasoning={"effort": "medium"}
)

The reasoning effort field is the parameter most teams overlook when migrating, and it is the single biggest lever for controlling both cost and latency on GPT-6.1 Sol specifically.

Pros and Cons of Each Model

GPT-6.1 Sol

• Pro: Largest published context window of the three, at 1,050,000 tokens.
• Pro: Statistically tied with the pricier Claude Opus 5.5 on BenchLM’s October 15 leaderboard under its 90% confidence-interval rule.
• Pro: Cached input pricing is 50% cheaper than its own predecessor, GPT-6 Sol.
• Con: Trails both Claude Sonnet 5.5 and Gemini 4 Argon on the AA Intelligence Index (51.8 vs. 56.0 and 52.6) at the identical price point as Sonnet 5.5.
• Con: No published Terminal-Bench 4.0 score was found in the sources reviewed, making direct agentic-coding comparison incomplete.

Claude Sonnet 5.5

• Pro: Leads both the AA Intelligence Index (56.0) and Terminal-Bench 4.0 (59.9%) among the three models compared here.
• Pro: Cache read pricing at $0.10 per million tokens is half of predecessor Sonnet 5’s rate.
• Pro: No announced price-increase deadline, unlike Gemini 4 Argon.
• Con: Trails Gemini 4 Argon on Humanity’s Last Exam (55.0% vs. 57.1%) and on both ModelCap Arena leaderboards.
• Con: Context window is marginally smaller than GPT-6.1 Sol’s (1,000,000 vs. 1,050,000 tokens).

Gemini 4 Argon

• Pro: #1 on both the Arena text and Arena coding leaderboards among 213 models tracked by ModelCap.
• Pro: #1 on the Vals Index, Google’s cited benchmark for finance, legal, and tax reasoning tasks.
• Pro: Leads the other two on Humanity’s Last Exam (57.1%).
• Con: Price doubles from $2/$10 to $4/$20 per million tokens after November 30, 2026, the only one of the three with an announced price increase.
• Con: Context window and max output token figures were not found published alongside the other two vendors’ equivalent specs.

The Verdict: Which Model Wins the October 2026 Match-Up

There is no single winner across every published metric, and treating this as a simple leaderboard race misses the actual decision most engineering teams face. The data supports three distinct, defensible picks depending on what you are optimizing for.

If the priority is agentic coding at the lowest sustainable price, Claude Sonnet 5.5 is the strongest pick in this comparison: it leads the AA Intelligence Index at 56.0 versus GPT-6.1 Sol’s 51.8 and Gemini 4 Argon’s 52.6, leads Terminal-Bench 4.0 at 59.9%, and costs exactly the same as GPT-6.1 Sol at $2/$10 per million tokens, with no price-increase deadline attached. If the priority is document-heavy enterprise reasoning in regulated domains like finance, legal, or tax, Gemini 4 Argon’s #1 Vals Index ranking is the most directly relevant published credential, but only for teams that have modeled the cost of the December 1 price doubling into their budget. If the priority is ingesting the largest possible single document or codebase in one call, GPT-6.1 Sol’s 1,050,000-token context window is the largest published figure among the three, even though its raw reasoning scores on the AA Intelligence Index and Humanity’s Last Exam trail both competitors.

The broader signal, consistent with the Frontier Model Index analysis showing the top model cluster compressed to just 2 points apart, is that benchmark gaps between these three are now small enough that pricing structure, caching behavior, and deadline terms deserve at least as much weight as the leaderboard rank. For most teams evaluating these three models today, Claude Sonnet 5.5’s combination of leaderboard strength and stable pricing makes it the safer default, while Gemini 4 Argon remains the pick worth locking in before its price doubles, and GPT-6.1 Sol remains the context-window specialist of the group.

Frequently Asked Questions

Is Claude Sonnet 5.5 cheaper than GPT-6.1 Sol?
No, they are priced identically at $2 per million input tokens and $10 per million output tokens as of October 2026, according to both Anthropic’s and OpenAI’s own pricing documentation.

When does Gemini 4 Argon’s price increase take effect?
Google Cloud’s pricing announcement sets the increase for December 1, 2026, when the rate rises from $2/$10 to $4/$20 per million input/output tokens.

Which model has the largest context window?
GPT-6.1 Sol, with 1,050,000 tokens, published in OpenAI’s model documentation, slightly ahead of Claude Sonnet 5.5’s 1,000,000 tokens. Google has not published an equivalent figure for Gemini 4 Argon in the sources reviewed.

Which model scores highest on coding benchmarks?
On Terminal-Bench 4.0, Claude Sonnet 5.5 scored 59.9%, the highest published figure among the three in this comparison. On ModelCap’s Arena coding leaderboard, Gemini 4 Argon ranks #1 among 213 tracked models, a different measurement based on aggregated user preference rather than task-completion scoring.

Is Gemini 4 Argon good for finance and legal work?
It is the strongest of the three on this specific dimension, ranking #1 on the Vals Index, which Google cites as covering finance, coding, legal, and tax domains.

What is the reasoning.effort parameter on GPT-6.1 Sol?
It is OpenAI’s control for how much computation the model spends reasoning before responding, with five settings: low, medium (default), high, xhigh, and max, documented in OpenAI’s API model reference.

Do any of these three models support extended or adaptive thinking?
Yes. GPT-6.1 Sol uses an explicit reasoning.effort parameter, and Claude Sonnet 5.5 uses what Anthropic calls adaptive thinking, defaulting to a “high” effort setting. Google’s published documentation for Gemini 4 Argon does not detail an equivalent control in the sources reviewed for this article.

Should I wait for Gemini 4 Argon’s post-December pricing before deciding?
If your workload is price-sensitive, it is worth running your evaluation now while Argon’s introductory rate matches the other two models, then re-running your cost math against the $4/$20 standard rate before committing to it for production traffic past November 30, 2026.

Is Claude Opus 5.5 or GPT-6 Astra worth the extra cost over these three models?
Only for a narrow set of tasks based on the published data. Claude Opus 5.5 leads the AA Intelligence Index at 57.6 and Humanity’s Last Exam at 61.4%, both ahead of all three efficiency-tier models in this comparison, but the price premium means most routine coding and support workloads will not see a proportional benefit.

Can I use these benchmark scores to predict performance on my own workload?
Treat them as directional signals rather than guarantees. Terminal-Bench 4.0, Humanity’s Last Exam, and the AA Intelligence Index each rank these three models differently, which means your specific prompts and task types may align more closely with one benchmark’s methodology than another, and only testing against your own evaluation set will confirm which model performs best for your use case.