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Data

Grok 4.20 vs Beam 1.0 501B-A23B FP8

Updated October 16, 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

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
xAI logo

xAI

57.42/100

Estimated · Public rank #60

Conditional range 47.7–67.2

Model B

Reflection AI

—

Evidence status unavailable

90% interval unavailable

Shared results
5
Grok 4.20 only
19
Beam 1.0 501B-A23B FP8 only
14
Like-for-like categories
0 / 8
Estimated: Grok 4.20. Conditional ranges do not establish rank confidence.How 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Beam 1.0 501B-A23B FP8 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Beam 1.0 501B-A23B FP8 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented
  • 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

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

    Confidence: listed-rates
  • 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

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.

21.6Grok 4.20—Beam 1.0 501B-A23B FP8

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Not comparable
Grok 4.20
25.1
Supported · #103/128
Beam 1.0 501B-A23B FP8
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 6 public rows
Reading
Not comparable

Coding

Not comparable
Grok 4.20
21.6
Supported · #118/150
Beam 1.0 501B-A23B FP8
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
Grok 4.20
44.9
Unranked · 2 rankable rows
Beam 1.0 501B-A23B FP8
85.8
Unranked · 3 rankable rows
Basis
Provisional lane · 2 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Grok 4.20
53.4
#47/54
Beam 1.0 501B-A23B FP8
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Grok 4.20
53.0
Supported · #69/184
Beam 1.0 501B-A23B FP8
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Grok 4.20
Not ranked
Beam 1.0 501B-A23B FP8
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Grok 4.20
Not ranked
Beam 1.0 501B-A23B FP8
89.8
#28/148
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Grok 4.20
Not ranked
Beam 1.0 501B-A23B FP8
87.9
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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

Grok 4.20
$0.0025
Fits in one request
Beam 1.0 501B-A23B FP8
API rate not published
Fits in one request

Beam 1.0 501B-A23B FP8 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Grok 4.20
$0.07
Fits in one request
Beam 1.0 501B-A23B FP8
API rate not published
Fits in one request

Beam 1.0 501B-A23B FP8 has no comparable published API token rate.

Cache-heavy agent loop

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

Grok 4.20
$0.09
Fits in one request
Beam 1.0 501B-A23B FP8
API rate not published
Fits in one request
Cached-input rate unavailable

Beam 1.0 501B-A23B FP8 has no comparable published API token rate.

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

Grok 4.20

Not sourced

Beam 1.0 501B-A23B FP8

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

$0.2 per 1M cached input tokens

Beam 1.0 501B-A23B FP8

No comparable hosted API rate

Reflection Hugging Face model card

Reasoning profile

Grok 4.20

Reasoning

Beam 1.0 501B-A23B FP8

Reasoning

Weight access

Grok 4.20

Proprietary

Beam 1.0 501B-A23B FP8

Open Weight

License

Grok 4.20

Proprietary

Beam 1.0 501B-A23B FP8

Open Weight

Release date

Grok 4.20

2026-03-10

Beam 1.0 501B-A23B FP8

2026-10-16

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Grok 4.20 or Beam 1.0 501B-A23B FP8?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Grok 4.20 or Beam 1.0 501B-A23B FP8?

Beam 1.0 501B-A23B FP8 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Grok 4.20 or Beam 1.0 501B-A23B FP8?

Beam 1.0 501B-A23B FP8 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Grok 4.20 or Beam 1.0 501B-A23B FP8?

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 or Beam 1.0 501B-A23B FP8?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence38 rows

Agentic

  • Terminal-Bench 2.0

    Grok 4.2047.1%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • DeepSearchQA

    Grok 4.2062.8%
    Source
    Beam 1.0 501B-A23B FP879.6%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • Gert Labs

    Grok 4.2038.36%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Grok 4.2044.2%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • MazeBench

    Grok 4.200%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • τ³-bench results

    Grok 4.20—
    Beam 1.0 501B-A23B FP836.8%
    Source

    Not directly comparable

  • HLE w/ tools

    Grok 4.20—
    Beam 1.0 501B-A23B FP851.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Grok 4.20—
    Beam 1.0 501B-A23B FP878.9%
    Source

    Not directly comparable

  • MCP Atlas

    Grok 4.20—
    Beam 1.0 501B-A23B FP881.5%
    Source

    Not directly comparable

  • BrowseComp

    Grok 4.20—
    Beam 1.0 501B-A23B FP876.7%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Grok 4.2074.2%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • SWE-bench Verified

    Grok 4.2076.7%
    Source
    Beam 1.0 501B-A23B FP880.2%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • SWE-bench Pro

    Grok 4.2051.8%
    Source
    Beam 1.0 501B-A23B FP865.9%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • Vibe Code Bench

    Grok 4.204.06%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • LiveCodeBench (Vals)

    Grok 4.2084.3%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • SWE-bench (Vals)

    Grok 4.2072.2%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • DeepSWE

    Grok 4.20—
    Beam 1.0 501B-A23B FP843.0%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Grok 4.20—
    Beam 1.0 501B-A23B FP878.9%
    Source

    Not directly comparable

  • SWE-Atlas Codebase QnA

    Grok 4.20—
    Beam 1.0 501B-A23B FP832.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Grok 4.20—
    Beam 1.0 501B-A23B FP878.1%
    Source

    Not directly comparable

  • SciCode

    Grok 4.20—
    Beam 1.0 501B-A23B FP848.3%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Grok 4.2053.3%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • ARC-AGI-3

    Grok 4.200.1%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • CritPt

    Grok 4.20—
    Beam 1.0 501B-A23B FP817.4%
    Source

    Not directly comparable

  • LongBench v2

    Grok 4.20—
    Beam 1.0 501B-A23B FP867.6%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Grok 4.2075.2%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • CharXiv

    Grok 4.2060.9%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • ERQA

    Grok 4.2054.1%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • SimpleVQA

    Grok 4.2057.4%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • MedXpertQA (MM)

    Grok 4.2065.8%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

Knowledge

  • GPQA-D

    Grok 4.2088.5%
    Source
    Beam 1.0 501B-A23B FP890.3%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • HLE w/o tools

    Grok 4.2031.6%
    Source
    Beam 1.0 501B-A23B FP836%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • HealthBench Hard

    Grok 4.2020.3%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • MedXpertQA (Text)

    Grok 4.2050.2%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • GPQA Diamond (Vals)

    Grok 4.2088.6%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

  • MMLU-Pro (Vals)

    Grok 4.2086.3%
    Source
    Beam 1.0 501B-A23B FP8—

    Not directly comparable

Instruction following

  • IFBench

    Grok 4.20—
    Beam 1.0 501B-A23B FP880.7%
    Source

    Not directly comparable

Math

  • AIME26

    Grok 4.20—
    Beam 1.0 501B-A23B FP898.0%
    Source

    Not directly comparable

38 public results · 5 shared

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Last updated October 16, 2026