Data as of November 13, 2026 · How the score is built
Gemini 4 Argon
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.
| Token type | Price 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.
Where Gemini 4 Argon ranks by benchmark score:
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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 | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #6 of 135Percentile 96thWeight 22%13 benchmarksVerified | 74.9 | 13 benchmarks | Verified | |||
| CodingRank #9 of 162Percentile 95thWeight 20%9 benchmarksVerified | 71.8 | 9 benchmarks | Verified | |||
| ReasoningRank #7 of 160Percentile 96thWeight 17%7 benchmarksVerified | 76.5 | 7 benchmarks | Verified | |||
| MultimodalWeight 12%5 benchmarksVerified | Score pending | 5 benchmarks | Verified | |||
| KnowledgeRank #8 of 180Percentile 96thWeight 12%2 benchmarksVerified | 79.2 | 2 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%1 benchmarkVerified | Score pending | 1 benchmark | Verified | |||
| MathRank Not rankedWeight 5%2 benchmarksVerified | 87.4 | 2 benchmarks | Verified |
59 of 919 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow 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.
- Agentic13/13 verified
- Coding9/9 verified
- Reasoning7/7 verified
- Multimodal5/5 verified
- Knowledge2/2 verified
- MultilingualNot measured
- Inst. Following1/1 verified
- Math2/2 verified
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
- Agentic#6/135
- Coding#9/162
- Reasoning#7/160
- MultimodalNot ranked
- Knowledge#8/180
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
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.
Coding9 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| DeepSWE | 77.9% | Best verified: Muse Wonder Full · 79.0% | Gap1.1 behind | Reported by Google | Counts15% ref. weight |
| CursorBench 4.0 | 52.9% | Best verified: Gemini 4 Carbon · 60.4% | Gap7.5 behind | Run by CursorBench 4.0 | Counts10% ref. weight |
| FrontierSWE v2 | 55.0% | Best verified: Claude Fable 5.2 · 69.9% | Gap14.9 behind | Run by FrontierSWE v2 | Counts8% ref. weight |
| FrontierCode 2.0 | 26.3% | Best verified: Claude Fable 5.2 · 49.1% | Gap22.8 behind | Run by FrontierCode 2.0 | Counts4% ref. weight |
| ScarfBenchScarfBench: Cross-Framework Application Migration in Enterprise Java | 75.2% | Best verified: Gemini 4 Argon · 75.2% | GapBest verified | Run by ScarfBench | Shown only |
| Vibe Code BenchVibe Code Bench v1.1 | 91.90% | Best verified: Gemini 4 Argon · 91.90% | GapHighest listed | Reported by Google | Shown only |
| PostTrainBench v1.1 | 45.3% | Best verified: Claude Opus 5.5 · 49.3% | Gap4 behind | Reported by Google | Shown only |
| Bug Hunt Bench | 40.7 fixes | Best verified: Claude Fable 5.2 · 58.0 fixes | Gap17.3 behind | Run by Bug Hunt Bench | Shown only |
| PostTrainBenchPostTrainBench v1.2 | 40.4% | Best verified: Claude Opus 5.5 · 43.8% | Gap3.4 behind | Run by PostTrainBench | Shown only |
Agentic13 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| Terminal-Bench 4.0 | 57.40% | Best verified: Claude Sonnet 5.5 · 70.60% | Gap13.2 behind | Reported by Google | Counts9% ref. weight |
| AutomationBench | 51.3% | Best verified: MiniMax M3.1 Flash · 62.2% | Gap10.9 behind | Reported by Google | Counts6% ref. weight |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | 57.7% | Best verified: Claude Opus 5.5 · 87.6% | Gap29.9 behind | Independent run: Vals AI | Counts4% ref. weight |
| Agents' Last Exam | 39.5% | Best verified: GPT-6.1 Terra · 60.0% | Gap20.5 behind | Reported by Google | Counts3% ref. weight |
| Finance Agent v2 | 65.4% | Best verified: Gemini 4 Argon · 65.4% | GapHighest listed | Reported by Google | Shown only |
| OSWorld 2.0 | 69.2% | Best verified: Kimi K3.1 Pro · 79.6% | Gap10.4 behind | Reported by Google | Shown only |
| CWE-bench v1 | 68.0% | Best verified: Gemini 4 Carbon · 71.0% | Gap3 behind | Run by CWE-bench v1 | Shown only |
| Terminal-Bench-Science 0.1 (6x verifier timeout)Terminal-Bench-Science 0.1 with sixfold verifier timeout | 57.6% | Best verified: Gemini 4 Argon · 57.6% | GapHighest listed | Reported by Google | Shown only |
| MazeBenchMazeBench: visual spatial reasoning in a 3D open world | 12% | Best verified: GPT-6 Astra · 23% | Gap11 behind | Run by MazeBench | Shown only |
| OSWorld 2.1 OfflineOSWorld 2.1 offline subset (82 tasks) | 74.7% | Best verified: Claude Fable 5.2 · 89.3% | Gap14.6 behind | Reported by Google | Shown only |
| ExploitGym | 39.5% | Best verified: Gemini 4 Carbon · 53.5% | Gap14 behind | Reported by Google | Shown only |
| RoboDojo Sim | 34.2% | Best verified: Claude Fable 5.2 · 64.9% | Gap30.7 behind | Lab-reported, from another publisher’s document | Shown only |
| CWE-benchCWE-bench v2 | 65.0% | Best verified: Claude Fable 5.2 · 75.0% | Gap10 behind | Run by CWE-bench | Shown only |
Reasoning7 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2 | 91.3% | Best verified: GPT-6 Astra · 95% | Gap3.8 behind | Run by ARC-AGI-2 | Counts15% ref. weight |
| ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3 | 46.3% | Best verified: Claude Fable 5.2 · 65.7% | Gap19.4 behind | Run by ARC-AGI-3 | Counts15% ref. weight |
| STEMBenchSTEMBench120: six-domain scientific research progress | 2.44 | Best verified: GPT-6 Astra · 2.81 | Gap0.4 behind | Run by STEMBench | Shown only |
| Graphwalks BFS 128KGraphwalks BFS 0K-128K | 99.7% | Best verified: Gemini 4 Argon · 99.7% | GapHighest listed | Reported by Google | Shown only |
| GraphWalks BFS 256K–1M | 84.2% | Best verified: Gemini 4 Carbon · 96.2% | Gap12 behind | Reported by Google | Shown only |
| ARC-AGI-1ARC-AGI-1 Semi-Private Evaluation | 98.50% | Best verified: Claude Fable 5 · 98.50% | GapBest verified | Run by ARC-AGI-1 | Shown only |
| ARC-AGI-3 Provider AdapterARC-AGI-3 Semi-Private: Provider Adapter harness | 97.30% | Best verified: GPT-6 Astra · 98.55% | Gap1.2 behind | Run by ARC-AGI-3 Provider Adapter | Shown only |
Multimodal5 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| ParseBench-2ParseBench-2: severity-aware document parsing (LlamaIndex leaderboard) | 84.20% | Best verified: Claude Opus 5.5 · 85.73% | Gap1.5 behind | Run by ParseBench-2 | Shown only |
| ExtractBench ProExtractBench Pro: long-tail document extraction challenges (LlamaIndex) | 30% | Best verified: GPT-6 Astra · 58% | Gap28 behind | Run by ExtractBench Pro | Shown only |
| Chartography (no tools)Chartography without tools | 71.6% | Best verified: Claude Fable 5.2 · 73.4% | Gap1.8 behind | Reported by Google | Shown only |
| LVBench | 91.7% | Best verified: Gemini 4 Argon · 91.7% | GapHighest listed | Reported by Google | Shown only |
| Blueprint-Bench 2 | 54.4% | Best verified: Gemini 4 Carbon · 58.6% | Gap4.2 behind | Reported by Google | Shown only |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| LABBench2LABBench2: An Improved Benchmark for AI Systems Performing Biology Research | 88.8% | Best verified: Gemini 4 Argon · 88.8% | GapHighest listed | Reported by Google | Shown only |
| Morphology-to-molecule matchingAxiom Bio morphology-to-molecule matching | 25.2% | Best verified: Claude Mythos 5.2 · 35.2% | Gap10 behind | Lab-reported, from another publisher’s document | Shown only |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attempts | 0.7% | Best verified: Gemini 4 Carbon · 0.0% | Gap0.7 behind | Reported by Google | Shown only |
Math2 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | 85.965% | Best verified: Claude Fable 5.2 · 95.088% | Gap9.1 behind | Run by FrontierMath v2 (Tiers 1-3) | CountsWeighted 30% |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | 78.049% | Best verified: Claude Fable 5.2 · 100.000% | Gap22 behind | Run by FrontierMath v2 (Tier 4) | CountsWeighted 10% |
External signals20 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| Game Arena Go | 1686 | Best verified: Claude Fable 5.2 · 1877 | Gap191 behind | Run by Game Arena Go | Shown only |
| ASCII Arena | 1983 | Best verified: Gemini 4 Carbon · 2019 | Gap36 behind | Run by ASCII Arena | Shown only |
| Landmark Arena | 1595 | Best verified: Claude Opus 5.5 · 1792 | Gap197 behind | Run by Landmark Arena | Shown only |
| Pelican Arena | 1664 | Best verified: GPT-6 Astra · 1819 | Gap155 behind | Run by Pelican Arena | Shown only |
| FrontierPhysicsEnd-to-End Physics Research Benchmark for AI Agents | 8.3% | Best verified: Claude Opus 5.5 · 27.4% | Gap19.1 behind | Run by FrontierPhysics | Shown only |
| NYT ConnectionsNYT Connections LLM Benchmark (extended version) | 98.6% | Best verified: Gemini 4 Carbon · 99.3% | Gap0.7 behind | Run by NYT Connections | Shown only |
| Joke Arena | 1501 | Best verified: GPT-6 Astra · 1564 | Gap63 behind | Run by Joke Arena | Shown only |
| Chart Arena | 1521 | Best verified: GPT-6.1 Sol · 1627 | Gap106 behind | Run by Chart Arena | Shown only |
| Landing Page Arena | 1393 | Best verified: GPT-6.1 Sol · 1691 | Gap298 behind | Run by Landing Page Arena | Shown only |
| Code2Video | 1539 | Best verified: Claude Opus 5.5 · 1585 | Gap46 behind | Run by Code2Video | Shown only |
| DAYJOB Healthcare | 11.6% | Best verified: Claude Opus 5.5 · 25.2% | Gap13.6 behind | Run by DAYJOB Healthcare | Shown only |
| DAYJOB | 15.7% | Best verified: Claude Opus 5.5 · 24.5% | Gap8.8 behind | Run by DAYJOB | Shown only |
| FACTS Multimodal | 54.2% | Best verified: Gemini 4 Carbon · 60.2% | Gap6 behind | Run by FACTS Multimodal | Shown only |
| StealthMarkStealthMark Intelligence Benchmarks (overall score) | 67.4 | Best verified: GPT-6 Astra · 74.6 | Gap7.2 behind | Run by StealthMark | Shown only |
| SEC-Bench Pro | 83.4% | Best verified: Gemini 4 Carbon · 88.2% | Gap4.8 behind | Reported by Google | Shown only |
| Chess PuzzlesChess Puzzles (Epoch AI) | 59.0% | Best verified: Claude Fable 5.2 · 79.0% | Gap20 behind | Run by Chess Puzzles | Shown only |
| Mystery Game PuzzlesMystery Game Puzzles (Epoch AI) | 64.0% | Best verified: Claude Fable 5.2 · 90.0% | Gap26 behind | Run by Mystery Game Puzzles | Shown only |
| Furniture AssemblyFurniture Assembly Benchmark (Epoch AI) | 72.5% | Best verified: Claude Fable 5.2 · 90.0% | Gap17.5 behind | Run by Furniture Assembly | Shown only |
| EBR-benchEarthborne Rangers (EBR-bench) | 64.8% | Best verified: Claude Fable 5.2 · 90.5% | Gap25.7 behind | Run by EBR-bench | Shown only |
| MirrorCodeMirrorCode (Epoch AI and METR) | 58.9% | Best verified: Claude Sonnet 5.5 · 97.8% | Gap38.9 behind | Run by MirrorCode | Shown only |
Other lab-reported results2 rowsAnother metric, split or setting, or a test without a page
| Benchmark | Score | On a 0–100 scale | Who ran it | Use in ranking |
|---|---|---|---|---|
| Agents' Last Exam v1.1Averaged rubric score · 122-task release; not the binary pass rate | 69.0% | Reported by Google | Shown only | |
| ExploitGymSuccess rate · Ten-hour cap per task; the stored score uses the six-hour cap | 42.0% | Reported by Google | Shown 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.
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.
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.
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.
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- gemini-4-argonGoogle Gemini 4 Argon model documentation
- Context window
- 1MGoogle Gemini 4 Argon model documentation
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- text, image, video, audio, pdfGoogle Gemini 4 Argon model documentation
- Output modalities
- textGoogle 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
- Lifecycle
- activeGoogle Gemini 4 Argon model documentation
- 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.
| Host | Input /1M | Output /1M | Cached /1M | Context |
|---|---|---|---|---|
| OpenRouter | $2.00 | $10.00 | $0.10 | 1.05M |
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.