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Data

Data as of November 13, 2026 · How the score is built

Decision readingGemini 4 Argon scores 79 out of 100 and ranks #8 of 215. This profile shows 59 source-displayable benchmark rows; its strongest eligible category is Agentic at #6. API pricing is $2 input and $10 output per million tokens, with cached input at $0.1.

Email me when its price, rank or availability changes.

Capability

79/100

field median 49.4#8 of 215 ranked models

Public

#8of 215

Verified #7 of 81

Price

$2input / $10 output

input median $0.60cached $0.10 · batch + cache $0.050 · blended $6

Speed

83tok/s

field median 92 tok/sFirst token 44.96 s

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Holds badges in the November 2026 rankings release: #6 Overall, #7 Coding, #6 Reasoning, #4 Agentic, #9 Knowledge (rank badges)

Decision snapshot

Where this model sits in the ranked field, its strongest published evidence, and what to validate before choosing it. The marker compares it with the current ranked catalog, not with an absolute quality threshold.

Strongest published evidence

Agentic ranks #6. Particularly useful for coding agents, browser research, and computer-use workflows.

Validate before choosing

59 published rows leave some tracked benchmark slots empty. Coding is its lowest eligible category at #9.

Source-linked · 59 displayable benchmark rows

Gemini 4 Argon API pricing

Gemini 4 Argon costs $2.00 per million input tokens and $10.00 per million output tokens on the Gemini API. Cached input is $0.10 per million.

Gemini 4 Argon API pricing, per million tokens
Token typePrice per 1M tokens
Input$2.00
Cached input$0.10
Output$10.00

At these rates, a request with 10,000 input tokens and 1,000 output tokens costs $0.03 without caching.

On blended price (the average of the input and output rates), 5 of the 118 other ranked models with a published API price cost the same or less and score higher. See where it sits on the chart

Pricing notes: Google's Gemini API pricing page lists gemini-4-argon on the paid tier, not the Free tier. Standard input/output is $2.00 / $10.00 per million tokens through November 30, 2026, then $4.00 / $20.00 starting December 1; implicit cached input is $0.10 / $0.20 across those dates, and explicit caching is unsupported. Batch and Flex input/output is $1.00 / $5.00 through November 30, then $2.00 / $10.00 from December 1, with implicit cached input at $0.05 / $0.10. Priority input/output is $3.60 / $18.00 through November 30, then $7.20 / $36.00 from December 1, with implicit cached input at $0.18 / $0.36. An invite-only Contributor tier, whose content is used to improve Google's products, lists $1.00 / $5.00 with $0.05 cached input.

Rates from Google Gemini API pricing, effective September 30, 2026. API model ID: gemini-4-argon. Price registry updated November 12, 2026.

Is Gemini 4 Argon right for your job?

Its place on the LLM Selector’s job rankings, the same order the job pages publish. Describe your own job to see where it stands for that.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

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Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #6 of 135Percentile 96thWeight 22%13 benchmarksVerified
74.9
CodingRank #9 of 162Percentile 95thWeight 20%9 benchmarksVerified
71.8
ReasoningRank #7 of 160Percentile 96thWeight 17%7 benchmarksVerified
76.5
MultimodalWeight 12%5 benchmarksVerified
Score pending
KnowledgeRank #8 of 180Percentile 96thWeight 12%2 benchmarksVerified
79.2
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%1 benchmarkVerified
Score pending
MathRank Not rankedWeight 5%2 benchmarksVerified
87.4

59 of 919 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic13/13 verified
  2. Coding9/9 verified
  3. Reasoning7/7 verified
  4. Multimodal5/5 verified
  5. Knowledge2/2 verified
  6. MultilingualNot measured
  7. Inst. Following1/1 verified
  8. Math2/2 verified
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

Gemini 4 Argon category percentile values

  • Agentic96th percentile
  • Coding95th percentile
  • Reasoning96th percentile
  • MultimodalNot eligible
  • Knowledge96th percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#6/135
  2. Coding#9/162
  3. Reasoning#7/160
  4. MultimodalNot ranked
  5. Knowledge#8/180
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Every result says who ran the test. For this model, Google ran 21 of 61. Coding opens by default. The tick marks the best source-verified result in the catalog; provisional leaders do not set the reference.

Overall rank
#8
of 215 on the leaderboard
Reported by Google
21
Shown with that label
Other publishers
2
Another publisher’s document or table
Run by a benchmark
37
The benchmark’s own run
Independent runs
1
An outside evaluator’s run
Coding9 rows
Coding benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
DeepSWE77.9%

Best verified: Muse Wonder Full · 79.0%

Gap1.1 behindReported by GoogleCounts15% ref. weight
CursorBench 4.052.9%

Best verified: Gemini 4 Carbon · 60.4%

Gap7.5 behindRun by CursorBench 4.0Counts10% ref. weight
FrontierSWE v255.0%

Best verified: Claude Fable 5.2 · 69.9%

Gap14.9 behindRun by FrontierSWE v2Counts8% ref. weight
FrontierCode 2.026.3%

Best verified: Claude Fable 5.2 · 49.1%

Gap22.8 behindRun by FrontierCode 2.0Counts4% ref. weight
ScarfBenchScarfBench: Cross-Framework Application Migration in Enterprise Java75.2%

Best verified: Gemini 4 Argon · 75.2%

GapBest verifiedRun by ScarfBenchShown only
Vibe Code BenchVibe Code Bench v1.191.90%

Best verified: Gemini 4 Argon · 91.90%

GapHighest listedReported by GoogleShown only
PostTrainBench v1.145.3%

Best verified: Claude Opus 5.5 · 49.3%

Gap4 behindReported by GoogleShown only
Bug Hunt Bench40.7 fixes

Best verified: Claude Fable 5.2 · 58.0 fixes

Gap17.3 behindRun by Bug Hunt BenchShown only
PostTrainBenchPostTrainBench v1.240.4%

Best verified: Claude Opus 5.5 · 43.8%

Gap3.4 behindRun by PostTrainBenchShown only
Agentic13 rows
Agentic benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
Terminal-Bench 4.057.40%

Best verified: Claude Sonnet 5.5 · 70.60%

Gap13.2 behindReported by GoogleCounts9% ref. weight
AutomationBench51.3%

Best verified: MiniMax M3.1 Flash · 62.2%

Gap10.9 behindReported by GoogleCounts6% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run57.7%

Best verified: Claude Opus 5.5 · 87.6%

Gap29.9 behindIndependent run: Vals AICounts4% ref. weight
Agents' Last Exam39.5%

Best verified: GPT-6.1 Terra · 60.0%

Gap20.5 behindReported by GoogleCounts3% ref. weight
Finance Agent v265.4%

Best verified: Gemini 4 Argon · 65.4%

GapHighest listedReported by GoogleShown only
OSWorld 2.069.2%

Best verified: Kimi K3.1 Pro · 79.6%

Gap10.4 behindReported by GoogleShown only
CWE-bench v168.0%

Best verified: Gemini 4 Carbon · 71.0%

Gap3 behindRun by CWE-bench v1Shown only
Terminal-Bench-Science 0.1 (6x verifier timeout)Terminal-Bench-Science 0.1 with sixfold verifier timeout57.6%

Best verified: Gemini 4 Argon · 57.6%

GapHighest listedReported by GoogleShown only
MazeBenchMazeBench: visual spatial reasoning in a 3D open world12%

Best verified: GPT-6 Astra · 23%

Gap11 behindRun by MazeBenchShown only
OSWorld 2.1 OfflineOSWorld 2.1 offline subset (82 tasks)74.7%

Best verified: Claude Fable 5.2 · 89.3%

Gap14.6 behindReported by GoogleShown only
ExploitGym39.5%

Best verified: Gemini 4 Carbon · 53.5%

Gap14 behindReported by GoogleShown only
RoboDojo Sim34.2%

Best verified: Claude Fable 5.2 · 64.9%

Gap30.7 behindLab-reported, from another publisher’s documentShown only
CWE-benchCWE-bench v265.0%

Best verified: Claude Fable 5.2 · 75.0%

Gap10 behindRun by CWE-benchShown only
Reasoning7 rows
Reasoning benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
ARC-AGI-2Abstraction and Reasoning Corpus for AGI v291.3%

Best verified: GPT-6 Astra · 95%

Gap3.8 behindRun by ARC-AGI-2Counts15% ref. weight
ARC-AGI-3Abstraction and Reasoning Corpus for AGI v346.3%

Best verified: Claude Fable 5.2 · 65.7%

Gap19.4 behindRun by ARC-AGI-3Counts15% ref. weight
STEMBenchSTEMBench120: six-domain scientific research progress2.44

Best verified: GPT-6 Astra · 2.81

Gap0.4 behindRun by STEMBenchShown only
Graphwalks BFS 128KGraphwalks BFS 0K-128K99.7%

Best verified: Gemini 4 Argon · 99.7%

GapHighest listedReported by GoogleShown only
GraphWalks BFS 256K–1M84.2%

Best verified: Gemini 4 Carbon · 96.2%

Gap12 behindReported by GoogleShown only
ARC-AGI-1ARC-AGI-1 Semi-Private Evaluation98.50%

Best verified: Claude Fable 5 · 98.50%

GapBest verifiedRun by ARC-AGI-1Shown only
ARC-AGI-3 Provider AdapterARC-AGI-3 Semi-Private: Provider Adapter harness97.30%

Best verified: GPT-6 Astra · 98.55%

Gap1.2 behindRun by ARC-AGI-3 Provider AdapterShown only
Multimodal5 rows
Multimodal benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
ParseBench-2ParseBench-2: severity-aware document parsing (LlamaIndex leaderboard)84.20%

Best verified: Claude Opus 5.5 · 85.73%

Gap1.5 behindRun by ParseBench-2Shown only
ExtractBench ProExtractBench Pro: long-tail document extraction challenges (LlamaIndex)30%

Best verified: GPT-6 Astra · 58%

Gap28 behindRun by ExtractBench ProShown only
Chartography (no tools)Chartography without tools71.6%

Best verified: Claude Fable 5.2 · 73.4%

Gap1.8 behindReported by GoogleShown only
LVBench91.7%

Best verified: Gemini 4 Argon · 91.7%

GapHighest listedReported by GoogleShown only
Blueprint-Bench 254.4%

Best verified: Gemini 4 Carbon · 58.6%

Gap4.2 behindReported by GoogleShown only
Knowledge2 rows
Knowledge benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
LABBench2LABBench2: An Improved Benchmark for AI Systems Performing Biology Research88.8%

Best verified: Gemini 4 Argon · 88.8%

GapHighest listedReported by GoogleShown only
Morphology-to-molecule matchingAxiom Bio morphology-to-molecule matching25.2%

Best verified: Claude Mythos 5.2 · 35.2%

Gap10 behindLab-reported, from another publisher’s documentShown only
Inst. Following1 row
Inst. Following benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attempts0.7%

Best verified: Gemini 4 Carbon · 0.0%

Gap0.7 behindReported by GoogleShown only
Math2 rows
Math benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-385.965%

Best verified: Claude Fable 5.2 · 95.088%

Gap9.1 behindRun by FrontierMath v2 (Tiers 1-3)CountsWeighted 30%
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 478.049%

Best verified: Claude Fable 5.2 · 100.000%

Gap22 behindRun by FrontierMath v2 (Tier 4)CountsWeighted 10%
External signals20 rows
External signals benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
Game Arena Go1686

Best verified: Claude Fable 5.2 · 1877

Gap191 behindRun by Game Arena GoShown only
ASCII Arena1983

Best verified: Gemini 4 Carbon · 2019

Gap36 behindRun by ASCII ArenaShown only
Landmark Arena1595

Best verified: Claude Opus 5.5 · 1792

Gap197 behindRun by Landmark ArenaShown only
Pelican Arena1664

Best verified: GPT-6 Astra · 1819

Gap155 behindRun by Pelican ArenaShown only
FrontierPhysicsEnd-to-End Physics Research Benchmark for AI Agents8.3%

Best verified: Claude Opus 5.5 · 27.4%

Gap19.1 behindRun by FrontierPhysicsShown only
NYT ConnectionsNYT Connections LLM Benchmark (extended version)98.6%

Best verified: Gemini 4 Carbon · 99.3%

Gap0.7 behindRun by NYT ConnectionsShown only
Joke Arena1501

Best verified: GPT-6 Astra · 1564

Gap63 behindRun by Joke ArenaShown only
Chart Arena1521

Best verified: GPT-6.1 Sol · 1627

Gap106 behindRun by Chart ArenaShown only
Landing Page Arena1393

Best verified: GPT-6.1 Sol · 1691

Gap298 behindRun by Landing Page ArenaShown only
Code2Video1539

Best verified: Claude Opus 5.5 · 1585

Gap46 behindRun by Code2VideoShown only
DAYJOB Healthcare11.6%

Best verified: Claude Opus 5.5 · 25.2%

Gap13.6 behindRun by DAYJOB HealthcareShown only
DAYJOB15.7%

Best verified: Claude Opus 5.5 · 24.5%

Gap8.8 behindRun by DAYJOBShown only
FACTS Multimodal54.2%

Best verified: Gemini 4 Carbon · 60.2%

Gap6 behindRun by FACTS MultimodalShown only
StealthMarkStealthMark Intelligence Benchmarks (overall score)67.4

Best verified: GPT-6 Astra · 74.6

Gap7.2 behindRun by StealthMarkShown only
SEC-Bench Pro83.4%

Best verified: Gemini 4 Carbon · 88.2%

Gap4.8 behindReported by GoogleShown only
Chess PuzzlesChess Puzzles (Epoch AI)59.0%

Best verified: Claude Fable 5.2 · 79.0%

Gap20 behindRun by Chess PuzzlesShown only
Mystery Game PuzzlesMystery Game Puzzles (Epoch AI)64.0%

Best verified: Claude Fable 5.2 · 90.0%

Gap26 behindRun by Mystery Game PuzzlesShown only
Furniture AssemblyFurniture Assembly Benchmark (Epoch AI)72.5%

Best verified: Claude Fable 5.2 · 90.0%

Gap17.5 behindRun by Furniture AssemblyShown only
EBR-benchEarthborne Rangers (EBR-bench)64.8%

Best verified: Claude Fable 5.2 · 90.5%

Gap25.7 behindRun by EBR-benchShown only
MirrorCodeMirrorCode (Epoch AI and METR)58.9%

Best verified: Claude Sonnet 5.5 · 97.8%

Gap38.9 behindRun by MirrorCodeShown only
Other lab-reported results2 rowsAnother metric, split or setting, or a test without a page
Results the lab reported, with the metric and setup, who ran each test, and that none counts toward a rank
Agents' Last Exam v1.1Averaged rubric score · 122-task release; not the binary pass rate69.0%Reported by GoogleShown only
ExploitGymSuccess rate · Ten-hour cap per task; the stored score uses the six-hour cap42.0%Reported by GoogleShown only

Outlined bars are lab-reported. Filled bars are benchmark-run or independent.

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Current modelExplore all models

Gemini 4 Argon · 79.0 score · $6 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

405060708090100$0.10$0.50$1$5$10$25$50$100↘ frontierGemini 4 Argon

Horizontal: blended price per million tokens, log scale · Vertical: public score

Published price history

This chart appears only when at least two dated first-party price records exist for the exact model. It shows rate changes, not an inferred cost trend.

06122026-102026-102026-11Output priceInput price

3 dated first-party price records. The newest record lists $10 per million tokens.

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Feb 19, 2026

    Gemini 3.1 Pro

    Score 63.1 · $2 / $12

  2. Sep 30, 2026 · you are here

    Gemini 4 Argon

    Score 79.0 · $2 / $10

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
text, image, video, audio, pdfGoogle Gemini 4 Argon model documentation
Parameters
Not disclosed by the provider
Availability
Gemini API · Google AI StudioGoogle Gemini 4 Argon model documentation
Cloud regions
Not tracked yet
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Published at $0.10 per million cached input tokensGoogle Gemini API pricing
Self-host
Weights are not published
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

Gemini 4 Argon ranks #8 of 215 on the public leaderboard with a score of 79.03/100. Its source-verified position is #7 of 81.

Gemini 4 Argon is a proprietary model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Google lists Gemini 4 Argon as a stable Gemini API model, available through Gemini API and Google AI Studio on the paid tier; it is not offered on the Free tier.

Google announced Gemini 4 Argon on September 30, 2026. Its current model documentation specifies a unified 1,048,576-token input/output limit, a 65,536-token synchronous output limit, and up to 1,048,576 output tokens with long decode. Exact launch scores use the highest thinking settings unless noted. OSWorld 2.0 is the offline-subset partial score, maximized over three runs; Terminal-Bench-Science 0.1 uses a sixfold verifier timeout. PostTrainBench v1.1 and CWE-bench v1 remain separate from older benchmark versions. Vals Index (68.9%), Harvey legal (19.6%), and RiemannBench (76.0%) remain outside scored fields because the launch does not establish an exact compatible catalog protocol for each. Private vulnerability and prompt-injection charts are not ranking inputs. Google's October 28, 2026 Gemini 4 Carbon launch adds Argon comparison rows run the same way: OSWorld 2.1 offline partial score 74.7%, ExploitGym 39.5% at the six-hour cap (the ten-hour result is listed in the benchmark ledger), SEC-bench Pro 83.4%, and Blueprint-Bench 2 54.4%. Its Agents' Last Exam v1.1 rubric score is not mapped, as for Carbon, and is listed in the benchmark ledger. BenchAlign determines placement from available source-backed evidence; provider-reported launch scores do not establish a Supported position.

Gemini 4 Argon sits in the Gemini 4 family with Gemini 4 Carbon, Gemini 4 Hydrogen. Its explicit predecessor is Gemini 3.1 Pro. 59 of 919 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Agentic at #6, while its lowest eligible position is Coding at #9. particularly useful for coding agents, browser research, and computer-use workflows.

Last updated November 13, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Where to run Gemini 4 Argon by API

OpenRouter is the one host we track that publishes a per-token price for it. Prices read from each host’s own pages on November 11, 2026.

Inference hosts for Gemini 4 Argon with input, output and cached-input prices per million tokens.
HostInput /1MOutput /1MCached /1MContext
OpenRouter$2.00$10.00$0.101.05M
Compare inference hosts

Questions

How does Gemini 4 Argon perform overall in AI benchmarks?

Gemini 4 Argon ranks #8 out of 215 models on the public BenchAlign leaderboard, with a score of 79.03/100. Its evidence status is Supported, and this profile shows 59 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is Gemini 4 Argon good for knowledge and understanding?

Gemini 4 Argon ranks #8 out of 180 eligible models for knowledge and understanding, with a public category score of 79.2/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Gemini 4 Argon good for coding and programming?

Gemini 4 Argon ranks #9 out of 162 eligible models for coding and programming, with a public category score of 71.8/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Gemini 4 Argon good for mathematics?

Gemini 4 Argon has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Gemini 4 Argon good for reasoning and logic?

Gemini 4 Argon ranks #7 out of 160 eligible models for reasoning and logic, with a public category score of 76.5/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Gemini 4 Argon good for agentic tool use and computer tasks?

Gemini 4 Argon ranks #6 out of 135 eligible models for agentic tool use and computer tasks, with a public category score of 74.9/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Gemini 4 Argon good for multimodal and grounded tasks?

Gemini 4 Argon has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Gemini 4 Argon good for instruction following?

Gemini 4 Argon has source-displayable benchmark coverage for instruction following, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Which sibling models are related to Gemini 4 Argon?

Gemini 4 Argon belongs to the Gemini 4 family. Related tracked variants include Gemini 4 Carbon, Gemini 4 Hydrogen. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does Gemini 4 Argon have full benchmark coverage on BenchLM?

No. Gemini 4 Argon currently has 72 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of Gemini 4 Argon?

Gemini 4 Argon has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

Compare Gemini 4 Argon with every tracked model1032 comparisons