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BenchLM

Gemini 4 Argon vs Grok Build 0.1

Updated September 30, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo

Google

64.59/100

Estimated · Public rank #32

90% interval 53.1–76.1

Model B
xAI logo

xAI

—

Evidence status unavailable

90% interval unavailable

Shared results
0
Gemini 4 Argon only
17
Grok Build 0.1 only
1
Like-for-like categories
0 / 8
Estimated: Gemini 4 ArgonHow the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Grok Build 0.1

    Grok Build 0.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Grok Build 0.1

    Grok Build 0.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Grok Build 0.1

    Grok Build 0.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Grok Build 0.1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Gemini 4 Argon and Grok Build 0.1 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

68.4Gemini 4 Argon25.8Grok Build 0.1

Directional only · BenchAlign v5.7

Gemini 4 Argon scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
Gemini 4 Argon
63.7
Estimated · #14/119
Grok Build 0.1
28.9
Estimated · #85/119
Basis
BenchAlign v5.7 lane · 7 vs 1 public rows
Reading
Directional only

Coding

Directional only
Gemini 4 Argon
68.4
Supported · #8/144
Grok Build 0.1
25.8
Estimated · #107/144
Basis
BenchAlign v5.7 lane · 4 vs 0 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 4 Argon
77.1
Unranked · 3 rankable rows
Grok Build 0.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 4 Argon
Not ranked
Grok Build 0.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 4 Argon
72.9
Supported · #11/170
Grok Build 0.1
Not ranked
Basis
BenchAlign v5.7 lane · 1 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 4 Argon
Not ranked
Grok Build 0.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 4 Argon
Not ranked
Grok Build 0.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 4 Argon
Not ranked
Grok Build 0.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemini 4 Argon
$0.007
Fit state unavailable
Grok Build 0.1
$0.002
Fits in one request

Grok Build 0.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 4 Argon
$0.13
Fit state unavailable
Grok Build 0.1
$0.056
Fits in one request

Grok Build 0.1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemini 4 Argon
$0.16
Fit state unavailable
Grok Build 0.1
$0.08
Fits in one request

Grok Build 0.1 has the lower modeled cost

Costs use the listed standard API rates.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

API model ID

Gemini 4 Argon

Not sourced

Grok Build 0.1

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemini 4 Argon

$0.1 per 1M cached input tokens

Google Gemini 4 Argon announcement

Grok Build 0.1

$0.2 per 1M cached input tokens

Documented inputs

Gemini 4 Argon

Not sourced

Grok Build 0.1

Not sourced

Documented outputs

Gemini 4 Argon

Not sourced

Grok Build 0.1

Not sourced

Reasoning profile

Gemini 4 Argon

Reasoning

Grok Build 0.1

Non-Reasoning

Weight access

Gemini 4 Argon

Proprietary

Grok Build 0.1

Proprietary

License

Gemini 4 Argon

Proprietary

Grok Build 0.1

Proprietary

Release date

Gemini 4 Argon

2026-09-30

Grok Build 0.1

2026-05-20

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.13 vs $0.056. Cache-heavy agent loop: $0.16 vs $0.08.
Context tradeoff
A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 4 Argon or Grok Build 0.1?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 4 Argon or Grok Build 0.1?

Gemini 4 Argon scores higher for coding on the public lane, 68.4 to 25.8. Grok Build 0.1 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 4 Argon or Grok Build 0.1?

Gemini 4 Argon scores higher for agentic tasks on the public lane, 63.7 to 28.9. Gemini 4 Argon and Grok Build 0.1 are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemini 4 Argon or Grok Build 0.1?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.002 on Grok Build 0.1; repository review costs $0.13 and $0.056; the cache-heavy agent loop costs $0.16 and $0.08. Costs use the listed standard API rates.

Which has the larger context window, Gemini 4 Argon or Grok Build 0.1?

A complete documented context-window comparison is not available.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence18 rows

Agentic

  • AutomationBench

    Gemini 4 Argon51.3%
    Source
    Grok Build 0.1—

    Not directly comparable

  • Finance Agent v2

    Gemini 4 Argon65.4%
    Source
    Grok Build 0.1—

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 4 Argon57.40%
    Source
    Grok Build 0.1—

    Not directly comparable

  • Agents' Last Exam

    Gemini 4 Argon39.5%
    Source
    Grok Build 0.1—

    Not directly comparable

  • OSWorld 2.0

    Gemini 4 Argon69.2%
    Source
    Grok Build 0.1—

    Not directly comparable

  • CWE-bench v1

    Gemini 4 Argon68.0%
    Source
    Grok Build 0.1—

    Not directly comparable

  • Terminal-Bench-Science 0.1 (6x verifier timeout)

    Gemini 4 Argon57.6%
    Source
    Grok Build 0.1—

    Not directly comparable

  • Gert Labs

    Gemini 4 Argon—
    Grok Build 0.149.15%
    Source

    Not directly comparable

Coding

  • DeepSWE

    Gemini 4 Argon77.9%
    Source
    Grok Build 0.1—

    Not directly comparable

  • FrontierSWE v2

    Gemini 4 Argon55.1%
    Source
    Grok Build 0.1—

    Not directly comparable

  • Vibe Code Bench

    Gemini 4 Argon91.90%
    Source
    Grok Build 0.1—

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 4 Argon45.3%
    Source
    Grok Build 0.1—

    Not directly comparable

Reasoning

  • Graphwalks BFS 128K

    Gemini 4 Argon99.7%
    Source
    Grok Build 0.1—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 4 Argon84.2%
    Source
    Grok Build 0.1—

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Gemini 4 Argon71.6%
    Source
    Grok Build 0.1—

    Not directly comparable

  • LVBench

    Gemini 4 Argon91.7%
    Source
    Grok Build 0.1—

    Not directly comparable

Knowledge

  • LABBench2

    Gemini 4 Argon88.8%
    Source
    Grok Build 0.1—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 4 Argon0.7%
    Source
    Grok Build 0.1—

    Not directly comparable

18 public results · 0 shared

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Last updated September 30, 2026