Skip to main content
BenchLM
Data

Beam 1.0 501B-A23B FP8 vs Solar Pro 2

Updated October 16, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInAPI/MCP

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

Reflection AI

—

Evidence status unavailable

90% interval unavailable

Model B
Upstage logo

Upstage

29.02/100

Estimated · Public rank #181

Conditional range 14.7–43.4

Shared results
0
Beam 1.0 501B-A23B FP8 only
19
Solar Pro 2 only
0
Like-for-like categories
0 / 8
Estimated: Solar Pro 2. 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.

  • Long documents

    Prompts that approach the documented context limit

    Beam 1.0 501B-A23B FP8

    Beam 1.0 501B-A23B FP8 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

    Beam 1.0 501B-A23B FP8 and Solar Pro 2 are 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 and Solar Pro 2 are not ranked on the public lane for agentic, so no winner is named for agentic.

    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. Solar Pro 2 does not fit this workload in one request. Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Solar Pro 2 has no comparable published API token rate.

    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 FP8—Solar Pro 2

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.

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 v5.8 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
Solar Pro 2
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Solar Pro 2
Not ranked
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Beam 1.0 501B-A23B FP8
85.8
Unranked · 3 rankable rows
Solar Pro 2
25.9
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
Solar Pro 2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Solar Pro 2
28.0
Estimated · #161/184
Basis
BenchAlign v5.8 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Beam 1.0 501B-A23B FP8
Not ranked
Solar Pro 2
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
Solar Pro 2
38.2
Unranked · 1 rankable row
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
Solar Pro 2
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.

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
Solar Pro 2
API rate not published
Fits in one request

Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Solar Pro 2 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
Solar Pro 2
API rate not published
Fits in one request

Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Solar Pro 2 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
Solar Pro 2
API rate not published
Does not fit in one request
Cached-input rate unavailable

Solar Pro 2 does not fit this workload in one request. Beam 1.0 501B-A23B FP8 has no comparable published API token rate. Solar Pro 2 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

Solar Pro 2

Not sourced

Cached-input rate

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

Beam 1.0 501B-A23B FP8

No comparable hosted API rate

Reflection Hugging Face model card

Solar Pro 2

No comparable hosted API rate

Reasoning profile

Beam 1.0 501B-A23B FP8

Reasoning

Solar Pro 2

Reasoning

Weight access

Beam 1.0 501B-A23B FP8

Open Weight

Solar Pro 2

Proprietary

License

Beam 1.0 501B-A23B FP8

Open Weight

Solar Pro 2

Proprietary

Release date

Beam 1.0 501B-A23B FP8

2026-10-16

Solar Pro 2

Not sourced

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
Beam 1.0 501B-A23B FP8 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Beam 1.0 501B-A23B FP8 or Solar Pro 2?

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, Beam 1.0 501B-A23B FP8 or Solar Pro 2?

Beam 1.0 501B-A23B FP8 and Solar Pro 2 are 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 Solar Pro 2?

Beam 1.0 501B-A23B FP8 and Solar Pro 2 are 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 Solar Pro 2?

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 Solar Pro 2?

Beam 1.0 501B-A23B FP8 has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence19 rows

Agentic

  • τ³-bench results

    Beam 1.0 501B-A23B FP836.8%
    Source
    Solar Pro 2—

    Not directly comparable

  • HLE w/ tools

    Beam 1.0 501B-A23B FP851.1%
    Source
    Solar Pro 2—

    Not directly comparable

  • Terminal-Bench 2.1

    Beam 1.0 501B-A23B FP878.9%
    Source
    Solar Pro 2—

    Not directly comparable

  • MCP Atlas

    Beam 1.0 501B-A23B FP881.5%
    Source
    Solar Pro 2—

    Not directly comparable

  • BrowseComp

    Beam 1.0 501B-A23B FP876.7%
    Source
    Solar Pro 2—

    Not directly comparable

  • DeepSearchQA

    Beam 1.0 501B-A23B FP879.6%
    Source
    Solar Pro 2—

    Not directly comparable

Coding

  • DeepSWE

    Beam 1.0 501B-A23B FP843.0%
    Source
    Solar Pro 2—

    Not directly comparable

  • SWE-bench Pro

    Beam 1.0 501B-A23B FP865.9%
    Source
    Solar Pro 2—

    Not directly comparable

  • Terminal-Bench 2.1

    Beam 1.0 501B-A23B FP878.9%
    Source
    Solar Pro 2—

    Not directly comparable

  • SWE-Atlas Codebase QnA

    Beam 1.0 501B-A23B FP832.2%
    Source
    Solar Pro 2—

    Not directly comparable

  • SWE Multilingual

    Beam 1.0 501B-A23B FP878.1%
    Source
    Solar Pro 2—

    Not directly comparable

  • SWE-bench Verified

    Beam 1.0 501B-A23B FP880.2%
    Source
    Solar Pro 2—

    Not directly comparable

  • SciCode

    Beam 1.0 501B-A23B FP848.3%
    Source
    Solar Pro 2—

    Not directly comparable

Reasoning

  • CritPt

    Beam 1.0 501B-A23B FP817.4%
    Source
    Solar Pro 2—

    Not directly comparable

  • LongBench v2

    Beam 1.0 501B-A23B FP867.6%
    Source
    Solar Pro 2—

    Not directly comparable

Knowledge

  • GPQA-D

    Beam 1.0 501B-A23B FP890.3%
    Source
    Solar Pro 2—

    Not directly comparable

  • HLE w/o tools

    Beam 1.0 501B-A23B FP836%
    Source
    Solar Pro 2—

    Not directly comparable

Instruction following

  • IFBench

    Beam 1.0 501B-A23B FP880.7%
    Source
    Solar Pro 2—

    Not directly comparable

Math

  • AIME26

    Beam 1.0 501B-A23B FP898.0%
    Source
    Solar Pro 2—

    Not directly comparable

19 public results · 0 shared

Watch Beam 1.0 501B-A23B FP8 vs Solar Pro 2

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,600+ readers.

Last updated October 16, 2026