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Model comparison

Grok 4.20 Multi-agent vs o1-preview

Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Grok 4.20 Multi-agent

xAI

Evidence status unavailable

90% interval unavailable

o1-preview

OpenAI

Evidence status unavailable

90% interval unavailable

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 based on different benchmark sets are marked directional and do not name a winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    Grok 4.20 Multi-agent

    Grok 4.20 Multi-agent has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1-preview does not fit this workload in one request. o1-preview has no published cached-input rate, so cached tokens use its listed input rate. Grok 4.20 Multi-agent has no comparable published API token rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

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

Evidence parity totals are not available.
Shared results
0
Grok 4.20 Multi-agent only
0
o1-preview only
0
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Grok 4.20 Multi-agent
Not measured
o1-preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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

Grok 4.20 Multi-agent
API rate not published
Fits in one request
o1-preview
$0.045
Fits in one request

Grok 4.20 Multi-agent has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Grok 4.20 Multi-agent
API rate not published
Fits in one request
o1-preview
$0.93
Fits in one request

Grok 4.20 Multi-agent has no comparable published API token rate.

Cache-heavy agent loop

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

Grok 4.20 Multi-agent
API rate not published
Fits in one request
Cached-input rate unavailable
o1-preview
$3.90
Does not fit in one request
Cached input priced at the published list-input rate

o1-preview does not fit this workload in one request. o1-preview has no published cached-input rate, so cached tokens use its listed input rate. Grok 4.20 Multi-agent has no comparable published API token rate.

Specification differences

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

Context window

Maximum documented context; output-token limits may be lower.

Grok 4.20 Multi-agent

2M

o1-preview

200K

API model ID

Grok 4.20 Multi-agent

Not sourced

o1-preview

Not sourced

Cached-input rate

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

Grok 4.20 Multi-agent

No comparable hosted API rate

o1-preview

Not published

Documented inputs

Grok 4.20 Multi-agent

Not sourced

o1-preview

Not sourced

Documented outputs

Grok 4.20 Multi-agent

Not sourced

o1-preview

Not sourced

Provider availability

Grok 4.20 Multi-agent

Not sourced

o1-preview

Not sourced

Reasoning profile

Grok 4.20 Multi-agent

Reasoning

o1-preview

Reasoning

Weight access

Grok 4.20 Multi-agent

Proprietary

o1-preview

Proprietary

License

Grok 4.20 Multi-agent

Proprietary

o1-preview

Proprietary

Release date

Grok 4.20 Multi-agent

2026-03-10

o1-preview

2024-09-12

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Grok 4.20 Multi-agent has the larger documented window (2M).

Run the same representative tasks against both endpoints before changing production traffic.

Frequently asked questions

Which is better, Grok 4.20 Multi-agent or o1-preview?

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, Grok 4.20 Multi-agent or o1-preview?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Grok 4.20 Multi-agent or o1-preview?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Grok 4.20 Multi-agent or o1-preview?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Grok 4.20 Multi-agent or o1-preview?

Grok 4.20 Multi-agent has the larger documented context window: 2M, compared with 200K.

Related comparisons

Last updated July 29, 2026

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