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Beam 1.0 501B-A23B FP8 vs Step 5 Preview

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. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Beam 1.0 501B-A23B FP8

Reflection AI

—

Evidence status unavailable

90% interval unavailable

Model B
StepFun logo
Step 5 Preview

StepFun

69.45/100

Estimated · Public rank #20

Conditional range 59.7–79.2

Shared results
8
Beam 1.0 501B-A23B FP8 only
11
Step 5 Preview only
16
Like-for-like categories
0 / 8
Estimated: Step 5 Preview. 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.

—Beam 1.0 501B-A23B FP860.5Step 5 Preview

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.

Open the coding leaderboardShortlist models for this job
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
Beam 1.0 501B-A23B FP8
Not ranked
Step 5 Preview
65.3
Estimated · #18/128
Basis
BenchAlign v5.8 lane · 6 vs 13 public rows
Reading
Not comparable

Coding

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Step 5 Preview
60.5
Estimated · #20/150
Basis
BenchAlign v5.8 lane · 7 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
Beam 1.0 501B-A23B FP8
85.8
Unranked · 3 rankable rows
Step 5 Preview
83.4
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Step 5 Preview
74.6
#24/54
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Step 5 Preview
68.7
Supported · #22/184
Basis
BenchAlign v5.8 lane · 2 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Step 5 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Beam 1.0 501B-A23B FP8
89.8
#28/148
Step 5 Preview
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Beam 1.0 501B-A23B FP8
87.9
Unranked · 1 rankable row
Step 5 Preview
Not ranked
Basis
Provisional lane · 1 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 v5.8) 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

Beam 1.0 501B-A23B FP8
API rate not published
Fits in one request
Step 5 Preview
$0.00235
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

Beam 1.0 501B-A23B FP8
API rate not published
Fits in one request
Step 5 Preview
$0.0581
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

Beam 1.0 501B-A23B FP8
API rate not published
Fits in one request
Cached-input rate unavailable
Step 5 Preview
$0.057
Fits in one request

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.

Reasoning profile

Beam 1.0 501B-A23B FP8

Reasoning

Step 5 Preview

Reasoning

Weight access

Beam 1.0 501B-A23B FP8

Open Weight

Step 5 Preview

Open Weight

License

Beam 1.0 501B-A23B FP8

Open Weight

Step 5 Preview

Open Weight

Release date

Beam 1.0 501B-A23B FP8

2026-10-16

Step 5 Preview

2026-09-20

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
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, Beam 1.0 501B-A23B FP8 or Step 5 Preview?

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, Beam 1.0 501B-A23B FP8 or Step 5 Preview?

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, Beam 1.0 501B-A23B FP8 or Step 5 Preview?

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, Beam 1.0 501B-A23B FP8 or Step 5 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, Beam 1.0 501B-A23B FP8 or Step 5 Preview?

Both models list the same context window, 1M.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Beam 1.0 501B-A23B FP8
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Step 5 Preview
API / mo$2,775
Self-host / mo$27,962
Break-even617M/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence35 rows

Agentic

  • τ³-bench results

    Beam 1.0 501B-A23B FP836.8%
    Source
    Step 5 Preview—

    Not directly comparable

  • HLE w/ tools

    Beam 1.0 501B-A23B FP851.1%
    Source
    Step 5 Preview—

    Not directly comparable

  • Terminal-Bench 2.1

    Beam 1.0 501B-A23B FP878.9%
    Source
    Step 5 Preview85.0%
    Source

    Step 5 Preview leads this result

  • MCP Atlas

    Beam 1.0 501B-A23B FP881.5%
    Source
    Step 5 Preview85.6%
    Source

    Step 5 Preview leads this result

  • BrowseComp

    Beam 1.0 501B-A23B FP876.7%
    Source
    Step 5 Preview88.7%
    Source

    Step 5 Preview leads this result

  • DeepSearchQA

    Beam 1.0 501B-A23B FP879.6%
    Source
    Step 5 Preview—

    Not directly comparable

  • Terminal-Bench 4.0

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview33.30%
    Source

    Not directly comparable

  • CyberGym

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview84.7%
    Source

    Not directly comparable

  • AutomationBench

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview44.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview74.1%
    Source

    Not directly comparable

  • JobBench

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview59.0%
    Source

    Not directly comparable

  • APEX-Agents

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview37.8%
    Source

    Not directly comparable

  • DRACO

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview83.3%
    Source

    Not directly comparable

  • Agents' Last Exam

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview29.5%
    Source

    Not directly comparable

  • Hermes Index

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview32.8%
    Source

    Not directly comparable

  • Hermes Bench

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview69.2%
    Source

    Not directly comparable

Coding

  • DeepSWE

    Beam 1.0 501B-A23B FP843.0%
    Source
    Step 5 Preview67.7%
    Source

    Step 5 Preview leads this result

  • SWE-bench Pro

    Beam 1.0 501B-A23B FP865.9%
    Source
    Step 5 Preview—

    Not directly comparable

  • Terminal-Bench 2.1

    Beam 1.0 501B-A23B FP878.9%
    Source
    Step 5 Preview85.0%
    Source

    Step 5 Preview leads this result

  • SWE-Atlas Codebase QnA

    Beam 1.0 501B-A23B FP832.2%
    Source
    Step 5 Preview—

    Not directly comparable

  • SWE Multilingual

    Beam 1.0 501B-A23B FP878.1%
    Source
    Step 5 Preview—

    Not directly comparable

  • SWE-bench Verified

    Beam 1.0 501B-A23B FP880.2%
    Source
    Step 5 Preview—

    Not directly comparable

  • SciCode

    Beam 1.0 501B-A23B FP848.3%
    Source
    Step 5 Preview58.9%
    Source

    Step 5 Preview leads this result

  • ProgramBench

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview80.5%
    Source

    Not directly comparable

  • sweMarathon

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview72.7%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview40.5%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Beam 1.0 501B-A23B FP817.4%
    Source
    Step 5 Preview20.9%
    Source

    Step 5 Preview leads this result

  • LongBench v2

    Beam 1.0 501B-A23B FP867.6%
    Source
    Step 5 Preview—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview60.3%
    Source

    Not directly comparable

  • MMMU-Pro

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview76%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Beam 1.0 501B-A23B FP890.3%
    Source
    Step 5 Preview93.5%
    Source

    Step 5 Preview leads this result

  • HLE w/o tools

    Beam 1.0 501B-A23B FP836%
    Source
    Step 5 Preview—

    Not directly comparable

  • HLE

    Beam 1.0 501B-A23B FP8—
    Step 5 Preview46.5%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Beam 1.0 501B-A23B FP880.7%
    Source
    Step 5 Preview—

    Not directly comparable

Math

  • AIME26

    Beam 1.0 501B-A23B FP898.0%
    Source
    Step 5 Preview—

    Not directly comparable

35 public results · 8 shared

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Last updated October 16, 2026