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Beam 1.0 501B-A23B FP8 vs Spark-X2.6-4B

Updated October 20, 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. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Reflection AI

—

Evidence status unavailable

90% interval unavailable

Model B

SparkLLM

40.14/100

Estimated · Public rank #136

Conditional range 25.8–54.5

Shared results
13
Beam 1.0 501B-A23B FP8 only
6
Spark-X2.6-4B only
7
Like-for-like categories
1 / 8
Estimated: Spark-X2.6-4B. 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 FP830.6Spark-X2.6-4B

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.

Instruction following

Like-for-like
Beam 1.0 501B-A23B FP8
89.8
#28/149
Spark-X2.6-4B
78.2
#62/149
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Beam 1.0 501B-A23B FP8 leads

Agentic

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Spark-X2.6-4B
33.0
Estimated · #89/131
Basis
BenchAlign v5.8 lane · 6 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Spark-X2.6-4B
30.6
Estimated · #95/152
Basis
BenchAlign v5.8 lane · 7 vs 4 public rows
Reading
Not comparable

Reasoning

Not comparable
Beam 1.0 501B-A23B FP8
84.9
Unranked · 3 rankable rows
Spark-X2.6-4B
48.8
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Spark-X2.6-4B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Spark-X2.6-4B
43.0
Estimated · #109/187
Basis
BenchAlign v5.8 lane · 2 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Spark-X2.6-4B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Beam 1.0 501B-A23B FP8
87.9
Unranked · 1 rankable row
Spark-X2.6-4B
76.4
Unranked · 3 rankable rows
Basis
Provisional lane · 1 vs 2 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.

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
Spark-X2.6-4B
Self-hosted; infrastructure cost varies
Fits in one request

Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Spark-X2.6-4B 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
Spark-X2.6-4B
Self-hosted; infrastructure cost varies
Fits in one request

Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Spark-X2.6-4B 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
Spark-X2.6-4B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Spark-X2.6-4B 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

Beam 1.0 501B-A23B FP8

Not sourced

Spark-X2.6-4B

Not sourced

Reasoning profile

Beam 1.0 501B-A23B FP8

Reasoning

Spark-X2.6-4B

Reasoning

Weight access

Beam 1.0 501B-A23B FP8

Open Weight

Spark-X2.6-4B

Open Weight

License

Beam 1.0 501B-A23B FP8

Open Weight

Spark-X2.6-4B

Open Weight

Release date

Beam 1.0 501B-A23B FP8

2026-10-16

Spark-X2.6-4B

2026-10-19

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 Spark-X2.6-4B?

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 Spark-X2.6-4B?

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 Spark-X2.6-4B?

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 Spark-X2.6-4B?

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 Spark-X2.6-4B?

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.
Spark-X2.6-4B
API / mo$0
Self-host / mo$298
Break-even—
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 evidence26 rows

Agentic

  • τ³-bench results

    Beam 1.0 501B-A23B FP836.8%
    Source
    Spark-X2.6-4B34.7%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • HLE w/ tools

    Beam 1.0 501B-A23B FP851.1%
    Source
    Spark-X2.6-4B36.9%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • Terminal-Bench 2.1

    Beam 1.0 501B-A23B FP878.9%
    Source
    Spark-X2.6-4B—

    Not directly comparable

  • MCP Atlas

    Beam 1.0 501B-A23B FP881.5%
    Source
    Spark-X2.6-4B62.4%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • BrowseComp

    Beam 1.0 501B-A23B FP876.7%
    Source
    Spark-X2.6-4B53.1%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • DeepSearchQA

    Beam 1.0 501B-A23B FP879.6%
    Source
    Spark-X2.6-4B—

    Not directly comparable

  • BFCL v4

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B71.8%
    Source

    Not directly comparable

  • τ²-bench results

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B86.9%
    Source

    Not directly comparable

  • Claw-Eval

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B69.9%
    Source

    Not directly comparable

Coding

  • DeepSWE

    Beam 1.0 501B-A23B FP843.0%
    Source
    Spark-X2.6-4B—

    Not directly comparable

  • SWE-bench Pro

    Beam 1.0 501B-A23B FP865.9%
    Source
    Spark-X2.6-4B54.3%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • Terminal-Bench 2.1

    Beam 1.0 501B-A23B FP878.9%
    Source
    Spark-X2.6-4B—

    Not directly comparable

  • SWE-Atlas Codebase QnA

    Beam 1.0 501B-A23B FP832.2%
    Source
    Spark-X2.6-4B—

    Not directly comparable

  • SWE Multilingual

    Beam 1.0 501B-A23B FP878.1%
    Source
    Spark-X2.6-4B64.7%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • SWE-bench Verified

    Beam 1.0 501B-A23B FP880.2%
    Source
    Spark-X2.6-4B62.8%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • SciCode

    Beam 1.0 501B-A23B FP848.3%
    Source
    Spark-X2.6-4B36.8%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

Reasoning

  • CritPt

    Beam 1.0 501B-A23B FP817.4%
    Source
    Spark-X2.6-4B—

    Not directly comparable

  • LongBench v2

    Beam 1.0 501B-A23B FP867.6%
    Source
    Spark-X2.6-4B53.3%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

Knowledge

  • GPQA-D

    Beam 1.0 501B-A23B FP890.3%
    Source
    Spark-X2.6-4B77.1%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • HLE w/o tools

    Beam 1.0 501B-A23B FP836%
    Source
    Spark-X2.6-4B17.9%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • GPQA

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B69.9%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Beam 1.0 501B-A23B FP880.7%
    Source
    Spark-X2.6-4B75.8%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • IFEval

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B93.6%
    Source

    Not directly comparable

Math

  • AIME26

    Beam 1.0 501B-A23B FP898.0%
    Source
    Spark-X2.6-4B91.2%
    Source

    Beam 1.0 501B-A23B FP8 leads this result

  • HMMT Feb 2026

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B81.3%
    Source

    Not directly comparable

  • IMOAnswerBench

    Beam 1.0 501B-A23B FP8—
    Spark-X2.6-4B75.2%
    Source

    Not directly comparable

26 public results · 13 shared

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Last updated October 20, 2026