GPT-6.1 Terra vs Gemini 4 Argon: Benchmarks, Pricing and Which Is Better

The figures that decide it:

• $1 / $5 vs $2 / $10 per million tokens in 2026 list pricing, with Argon rising to $4 / $20 on December 1, 2026 (OpenAI and Google pricing pages).
• 57.4% vs 50.3% on Terminal-Bench 4.0, Argon ahead, both vendor-reported in 2026 (Google DeepMind and OpenAI).
• $0.40 vs $1.62 per Intelligence Index task in October 2026, Terra cheaper by 4 times (Artificial Analysis).
• 15% hallucination rate for Argon on AA-Omniscience in 2026, with no Terra figure yet (Artificial Analysis).
• Five input types for Argon, two for Terra, per each vendor’s 2026 model docs.

GPT-6.1 Terra vs Gemini 4 Argon: Which Model Wins in One Minute

Gemini 4 Argon is the stronger model and GPT-6.1 Terra is the far cheaper one, so for most teams running high-volume agents Terra is the better buy, while Argon is the pick for long-context, multimodal and knowledge-work jobs. That is the whole GPT-6.1 Terra vs Gemini 4 Argon verdict in one sentence. The rest of this piece shows the numbers behind it, which of those numbers you can trust, and which model fits your workload and budget.

As of October 26, 2026, neither vendor has published a head-to-head. Google’s benchmark table sets Argon against GPT-6 Astra and two Claude models and skips Terra. OpenAI’s Terra launch material compares Terra with its own lineup and Claude, and skips Argon.

They also sit in different tiers. Argon is Google’s flagship, introduced on September 30, 2026 and generally available since October 13, 2026. Terra arrived today, October 26, 2026, as OpenAI’s cheaper and faster model, priced at one-tenth of GPT-6 Astra per token. So the honest question is whether Argon’s extra capability is worth roughly 2 to 4 times the token price.

Where the scores line up cleanly, Argon is somewhat ahead. Where the invoices line up, Terra is much cheaper.

One caveat hangs over all of it. Terra’s evidence is launch-day evidence, almost entirely reported by OpenAI, and the Vals.ai leaderboards had no Terra entry as of October 24, 2026. Argon has had four weeks of independent measurement. Keep that imbalance in mind every time a Terra number looks good.

What Does GPT-6.1 Terra Cost Compared to Gemini 4 Argon?

GPT-6.1 Terra costs $1 per million input tokens and $5 per million output tokens as of October 26, 2026, half of Argon’s introductory $2 / $10 and a quarter of the $4 / $20 Argon will charge from December 1, 2026.

The list prices, all per million tokens:

• Terra: $1 input, $5 output, $0.05 cached input, per the GPT-6.1 Terra model page from OpenAI, checked October 26, 2026.
• Argon, intro: $2 input, $10 output through November 30, 2026, per the Gemini API pricing page.
• Argon, standard: $4 input, $20 output from December 1, 2026. Vals.ai already lists Argon at the standard rate, so some third-party cost math is already running on the higher figure.
• Argon cached input: 95% off the input price, which works out to about $0.10 at intro pricing.

Caching is where the two diverge in kind. Terra uses explicit caching, which means you decide what gets pinned and you pay a cache-write fee to pin it. Argon offers implicit caching only, and Google’s docs state plainly that explicit caching is unsupported. In practice that means a Terra deployment with a fat, stable system prompt can engineer its own discount, while an Argon deployment takes whatever the implicit cache happens to hit.

Then there’s the surcharge nobody reads until the bill arrives. Any Terra request with more than 272K input tokens costs 2 times the input rate and 1.5 times the output rate for the entire request. One oversized document in a batch job and the whole call reprices. Google’s Argon pricing table shows no equivalent long-prompt tier.

Token price and task cost are two different numbers, though, and this is the part that matters for a budget. Artificial Analysis, an independent benchmarking site, measured Terra at max effort at $0.40 per Intelligence Index task in October 2026 and Argon at high effort at $1.62 at intro pricing. At standard pricing its launch analysis estimates $3.98 per Argon task, because Argon burns roughly 62k output tokens per task against about 27k for GPT-6 Astra. Argon thinks longer, and you pay for every token of it.

The rule we apply at AlphaCorp AI when scoping an agent build is to budget per completed task instead of per token. On that measure the gap is about 4 times at Argon’s intro price and roughly 10 times after December 1, 2026, a much wider spread than the 2 to 4 times list prices suggest.

Fair enough, you might say, but Argon is a flagship and Terra is a mid-tier model. The closer Google price analogue is Gemini 3.8 Flash at $0.75 / $3.75 through December 31, 2026, rising to $1.50 / $7.50 after. Google hasn’t positioned Flash as Terra’s rival, and no benchmark pairs them.

How GPT-6.1 Terra and Gemini 4 Argon Score on Reasoning, Coding and Math Benchmarks

On the benchmarks that compare cleanly, Gemini 4 Argon beats GPT-6.1 Terra by about 7 points on Terminal-Bench 4.0 (57.4% vs 50.3%) and by 5 points on the Artificial Analysis Intelligence Index (53 vs 48), while Terminal-Bench Science is effectively a tie. Everything else is apples to oranges, and the vendor charts are built to make it so.

Why only two clean rows? Because a benchmark is only comparable when both vendors ran the same version and reported the same baseline. GPT-6 Astra is the control here. Both the DeepMind Gemini models page and OpenAI’s Terra launch chart put Astra at 58.2% on Terminal-Bench 4.0, so Argon’s 57.4% and Terra’s 50.3% at max effort sit on the same scale. Terra’s number comes from OpenAI’s October 26, 2026 launch post.

The Terminal-Bench Science row needs a flag. Google lists Argon at 57.6% and Astra at 68.1%. Terra’s 58.3% comes from a secondary transcription of OpenAI’s launch chart, with Astra matching at 68.1%, so the baselines agree. Treat the Terra figure as unconfirmed until OpenAI’s own page is readable. If it stands, the two models are a point apart.

Agents’ Last Exam is the trap. Google’s table gives Argon 39.5% and Astra 34.2%, while the OpenAI chart gives Terra 60.0% and Astra 59.3%. Astra can’t score both 34.2% and 59.3% on the same test, so the harnesses differ, and comparing 39.5% with 60.0% tells you nothing. OSWorld has the same problem with different version numbers. Math gets no comparable row from either side.

What Argon’s own table does show is a flagship that beat Astra on 14 of 19 Google-chosen rows, including DeepSWE v1.1 at 77.9% vs 74.1%, but lost FrontierSWE v2 at 55.0% vs 65.5%. OpenAI’s framing for Terra is simpler:

Terra matches Astra’s results at one-tenth the price. (OpenAI developer community announcement, October 26, 2026)

No independent measurement backs that claim yet, and the 50.3% vs 58.2% Terminal-Bench gap argues against taking it literally.

Honestly, the number I’d watch is hallucination. Artificial Analysis reports a 15% hallucination rate for Argon on AA-Omniscience against 51% for Astra, though Argon’s accuracy on the same test is lower, 50% vs 63%. It abstains more and guesses less. There is no Terra figure on that measure at all, which for a production agent is a bigger gap than any benchmark row.

Context Window, Multimodal Input and Speed Compared Head to Head

On context window the two models tie at roughly 1.05 million tokens, so Gemini 4 Argon wins this section on input types and long-context retrieval evidence, while GPT-6.1 Terra keeps the fresher knowledge cutoff and the finer reasoning controls. Speed is a draw by default, because there is no measured Terra figure yet.

The raw context numbers look identical and behave differently.

• Terra: a 1,050,000-token window, capped at 922K tokens of input and 128K of output, per OpenAI’s model docs as of October 26, 2026.
• Argon: 1,048,576 tokens shared between input and output, with output able to stretch toward 1M under what Google’s model docs call “long decode”.

That split matters for one kind of job. If you need the model to write something enormous, say a full migration plan for a codebase or a cleaned-up 400-page transcript, Argon’s shared budget lets output run far past Terra’s 128K ceiling. If you need to feed in a huge corpus and get a short answer back, both fit the same material.

Filling the window and using it well are separate skills. Google’s 2026 table has Argon at 84.2% on GraphWalks BFS across 256K to 1M tokens, against 71.8% for GPT-6 Astra. That is the best public evidence that Argon tracks relationships across a near-full context instead of just holding the text. OpenAI published no equivalent figure for Terra. For a retrieval pipeline, that single row matters more than most headline scores.

Input types are a clean Argon win. Terra takes text and images. Argon takes text, image, video, audio and PDF, and scores 91.7% on LVBench for long video against Astra’s 87.5% in Google’s table. Both return text only.

Two smaller differences cut the other way:

• Reasoning settings: Terra offers six levels (none, low, medium, high, xhigh, max). Argon offers three (low, medium, high). More levels means more room to trade cost for quality per request.
• Knowledge cutoff: Terra’s is June 1, 2026. Argon’s model card gives March 2026. Three months rarely matters. Then you ask about a library that shipped in April.

Speed is where I’d love to hand you a number and can’t. OpenAI says Terra finishes tasks more than 3 times faster than Astra and Sol on average, a claim with no independent check as of launch day. Artificial Analysis clocks Argon at 79.7 output tokens per second and lists Terra’s speed as not available. Run the arithmetic on Argon’s roughly 62k output tokens per task and 79.7 tokens per second, and you get about 13 minutes of pure generation for one hard task. Slow is a feature of thinking longer.

Agentic and Tool-Use Capabilities: Where Each Model Pulls Ahead

GPT-6.1 Terra pulls ahead on computer-use and terminal automation per dollar, and Gemini 4 Argon pulls ahead on knowledge-work agents in legal, finance and business-process tasks. Pick by the kind of agent you’re building.

Terra’s whole pitch is computer use. OpenAI reports 74.2% on OSWorld 2.1 (Astra scores 80.9% on the same chart) and a Terminal-Bench 4.0 climb from 21.5% for GPT-5.6 Terra to 50.3%, the largest generational jump in either lineup this year. It also ships inside Codex and ChatGPT Work, so a team already working there gets a computer-use model without a new vendor contract or a new harness.

Argon’s agent wins happen in the office rather than the terminal. From Google’s 2026 table and Vals.ai:

• AutomationBench: 51.3% against 41.4% for Astra.
• Harvey’s Legal Agent Benchmark: 19.6% against 5.4%, nearly 4 times Astra’s score, though both are low enough that a legal agent still needs a lawyer reviewing the output.
• Vals Index: 69.24%, ranked 3 of 27 on Vals.ai’s October 2026 page, with Terra not yet listed.
• CWE-bench v1: tied with Astra at 68.0%.
• Terminal-Bench 4.0 on Vals.ai: 57.58%, ranked 7th as of October 24, 2026.

Apart from the Vals rows, those are Google’s own picks. Even so, legal reasoning and process automation are exactly where a flagship’s extra thinking tokens plausibly earn their cost.

What a benchmark row hides is step 14 of a 20-step loop. An agent that burns around 62k output tokens per task at Argon’s rate turns a trivial tool call into a line item, and a model that pauses to reason at every step is slower at every step. Terra, by OpenAI’s account, matches Astra at a tenth of the price and runs more than 3 times faster, which in an agent loop compounds across every iteration. Neither claim has independent backing yet, but if even half of it holds, Terra is the model you let loose on volume.

So the trade reads like this. Argon is likelier to get the hard step right, and its 15% measured hallucination rate on AA-Omniscience is reassuring for an agent that acts on its own output. Terra gets through more loops for the same money, and when we scope agent builds at AlphaCorp AI, loop count usually decides the budget before accuracy does.

Where GPT-6.1 Terra and Gemini 4 Argon Each Fall Short in Real Workloads

GPT-6.1 Terra falls short on input types, independent evidence and where you can use it, while Gemini 4 Argon falls short on cost, caching and access. Neither list is short, and Argon’s is longer.

Terra’s gaps, as of October 26, 2026:

• Text and image input only. No audio, no video, no native PDF.
• Almost every number is OpenAI’s own. Vals.ai’s leaderboards had no Terra entry as of October 24, 2026, and Artificial Analysis lists its speed as unavailable.
• OpenAI’s GPT-6.1 Terra safety addendum rates the model Critical in cybersecurity and High in biological and chemical capability. The same document notes that ExploitBench may be contaminated and that Terra shows the highest evaluation awareness of the models compared, at 9.88%. A model that senses when it’s being tested is harder to benchmark honestly.
• Inside ChatGPT it appears in Work and Codex. The consumer chat surface is absent from OpenAI’s rollout list.

Argon’s gaps, mostly about money and gates:

• $1.62 per Intelligence Index task at intro pricing and $3.98 at standard, by Artificial Analysis’s October 2026 measurements, roughly 4 to 10 times Terra’s $0.40.
• The $2 / $10 intro price ends November 30, 2026, and doubles the next day.
• No explicit caching, so you can’t pin a system prompt and bank the discount.
• No free tier in the Gemini API, and in consumer apps it is limited to Google AI Pro and Ultra subscribers.
• Public Argon blocks offensive cyber tasks, with the unrestricted build reserved for vetted defenders in the Fairwind Program. Google’s model card says it cannot rule out crossing its cybersecurity, CBRN and stealth thresholds.
• Accuracy of 50% on AA-Omniscience against Astra’s 63%. The low hallucination rate comes partly from declining to answer.
• FrontierSWE v2 at 55.0% against Astra’s 65.5%, plus a trailing score on the OSWorld offline subset.

The caching gap is the one that annoys me. Argon’s implicit cache can still land a 95% input discount, but you can’t design for it, and a budget you can’t design for is a budget you can’t promise.

Security teams get the rawest deal of all. If your workload is penetration testing or exploit triage, Terra carries a Critical cyber rating and Argon refuses the task outright. Neither model is a straightforward buy there, and no amount of benchmark reading changes that.

Which Model Fits Which Team: Choosing by Use Case and Budget

Teams running high-volume agents, computer-use automation or anything billed per loop should choose GPT-6.1 Terra, and teams doing long-context retrieval, legal or finance agents, or video and audio work should choose Gemini 4 Argon. Budget settles the close calls, and the budget math moves hard on December 1, 2026.

Choose Gemini 4 Argon if:

• Your prompts routinely pass 256K tokens and the model has to reason across them. Argon’s 84.2% on GraphWalks BFS (Google, 2026) is the only long-context retrieval evidence either vendor has put out.
• You’re building legal, finance or business-process agents, where Argon posted 19.6% on Harvey’s Legal Agent Benchmark and 51.3% on AutomationBench against Astra’s 5.4% and 41.4%.
• Your inputs include video, audio or PDF files. Terra declines all three.
• A wrong answer costs you more than a slow one. A 15% hallucination rate on AA-Omniscience (Artificial Analysis, 2026) is the lowest measured figure in this comparison.
• You can carry $4 / $20 per million tokens from December 1, 2026 without reopening the contract.

Choose GPT-6.1 Terra if:

• You run thousands of agent or computer-use tasks a day. At $0.40 per task versus Argon’s $1.62 (Artificial Analysis, October 2026), Terra is 4 times cheaper now and roughly 10 times cheaper once Argon’s intro price ends.
• Latency shapes the product. OpenAI’s 3-times-faster claim is unverified, but Argon’s measured 79.7 tokens per second against about 62k output tokens per task is a known wait.
• Your developers already work in Codex or ChatGPT Work.
• You want a system-prompt cache you can plan around, at Terra’s $0.05 per million cached input tokens.

Consider something else if:

• Your workload is offensive security, where Argon refuses and Terra carries a Critical rating.
• You’re a cost-sensitive Google-stack team. Gemini 3.8 Flash at $0.75 / $3.75 through December 31, 2026 is the nearer price match, though nothing benchmarks it against Terra.

Run the arithmetic on 10,000 tasks a month. Using Artificial Analysis’s October 2026 per-task figures, that is $4,000 on Terra, $16,200 on Argon at intro pricing and $39,800 on Argon from December 1, 2026. A flagship’s edge on a hard legal task may be worth $35,800 a month. On a batch of routine browser tasks, it almost never is.

What Changed From the Previous GPT and Gemini Generations

GPT-6.1 Terra more than doubled its predecessor’s Terminal-Bench 4.0 score, from 21.5% for GPT-5.6 Terra to 50.3%, while Gemini 4 Argon arrived as Google’s new flagship and beat GPT-6 Astra on 14 of 19 rows in Google’s own 2026 table.

Terra’s generational jump is the bigger story on the OpenAI side. OpenAI’s developer community announcement for GPT-6.1 Terra on October 26, 2026 frames it as fast, capable computer use at a lower price, and the lineup pricing shows where it sits:

Notice that Argon’s $2 / $10 intro price matches Sol exactly, and its $4 / $20 standard price lands between Sol and Astra. Google priced its flagship against OpenAI’s middle. Terra undercuts all of it.

Argon’s path was slower and gated. Google introduced it on September 30, 2026 to trusted cyber defenders through the Fairwind Program, then opened general availability on October 13, 2026 as gemini-4-argon. Some coverage still describes the limited rollout, so check the date on anything you read about access.

Its 14 wins over Astra came with a tie on CWE-bench v1 at 68.0% and losses on FrontierSWE v2, Terminal-Bench Science and the OSWorld offline subset. One caution on that table: Google’s evaluation methodology document doesn’t state how the competitor rows were produced, so the Astra figures in it are Google’s runs rather than OpenAI’s. Where both vendors agree, as on Astra’s 58.2% Terminal-Bench 4.0 score, the number is solid. Elsewhere, read it as Google’s view.

Frequently Asked Questions About GPT-6.1 Terra vs Gemini 4 Argon

Is Gemini 4 Argon better than GPT-6.1 Terra?

On the comparable evidence, yes, by a modest margin. Argon leads Terminal-Bench 4.0 by about 7 points (57.4% vs 50.3%, vendor-reported, 2026) and the Artificial Analysis Intelligence Index by 5 (53 vs 48, October 2026). Argon also holds Vals.ai’s rank of 3 of 27 on the Vals Index at 69.24%, with no Terra entry yet. Better at the task is a different question from better value.

Is GPT-6.1 Terra cheaper than Gemini 4 Argon?

Yes, by 2 times on list price today and 4 times from December 1, 2026. Terra is $1 / $5 per million tokens against Argon’s $2 / $10 intro and $4 / $20 standard rate. Per completed task the gap is wider, $0.40 against $1.62, because Argon generates far more output.

Which has the bigger context window?

They are effectively equal. Terra’s 1,050,000-token window and Argon’s 1,048,576 differ by under 2,000 tokens. The real difference is output: Terra caps at 128K, while Argon can push output toward 1M from its shared budget.

Can Gemini 4 Argon process video?

Yes. Argon accepts video, audio, PDF, image and text input and scored 91.7% on LVBench in Google’s 2026 table. Terra accepts text and images only, and both models return text.

Is Gemini 4 Argon free?

No. Argon has no free tier in the Gemini API as of October 26, 2026, and consumer access requires a Google AI Pro or Ultra subscription.

Is GPT-6.1 Terra available in ChatGPT?

Partly. Terra is rolling out to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex, and in the API as gpt-6.1-terra. The consumer chat surface is absent from OpenAI’s launch-day rollout list.

When does Argon’s intro pricing end?

November 30, 2026. From December 1, 2026 the price doubles to $4 input and $20 output per million tokens, and cached input, at 95% off, doubles with it.

How to Run Your Own Side-by-Side Evaluation Before Committing

Run 50 to 100 of your own tasks through gpt-6.1-terra and gemini-4-argon at matched reasoning levels, and score them on cost per completed task, wall-clock time and failure type. That single afternoon of work beats every vendor chart in this comparison, because the only benchmark that predicts your bill is the one built from your workload.

The procedure we use before a model gets written into a contract:

1. Pull real tasks, failures included. Take the last 50 to 100 jobs your agent handled, keep the ugly ones, and record what a correct outcome looks like for each.
2. Match the effort settings. Terra’s “high” and Argon’s “high” are different dials. Run Terra at high and at max, Argon at high, and compare the nearest pairs instead of assuming the labels line up.
3. Log tokens per task, then price them. Multiply by the list rates, and run Argon twice: once at the $2 / $10 intro price and once at the $4 / $20 rate that starts December 1, 2026.
4. Time the full loop. Measure wall-clock from request to usable output across every step of an agent run. Argon’s roughly 62k output tokens per task at 79.7 tokens per second (Artificial Analysis, October 2026) shows up here long before it shows up on a scoreboard.
5. Classify every failure. A wrong answer, a refusal and a timeout cost different amounts to recover from. Count them separately.
6. Recheck the leaderboards in two weeks. Vals.ai and Artificial Analysis had no Terra entries on launch day. Once they fill in, your numbers get an independent sanity check.

One more tip from experience. Include at least five tasks that exceed 272K input tokens, because Terra’s long-prompt surcharge reprices the whole request and a cost model built on short prompts will miss it.

If you’d rather have someone who has run this exact comparison on production agents set up the harness and read the results with you, talk to AlphaCorp AI about an evaluation.