Kimi K2.7 Code 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.

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

Model A

Kimi K2.7 Code

Moonshot AI

49.51/100

Supported · Public rank #96

90% interval 42.3–56.7

Model B

Beam 1.0 501B-A23B FP8

Reflection AI

—

Evidence status unavailable

90% interval unavailable

Shared results: 1
Kimi K2.7 Code only: 12
Beam 1.0 501B-A23B FP8 only: 18
Like-for-like categories: 0 / 8

Supported: Kimi K2.7 CodeHow 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 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
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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

41.8Kimi K2.7 Code—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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Instruction following

Directional only

Kimi K2.7 Code: 76.6; #63/148
Beam 1.0 501B-A23B FP8: 89.8; #28/148
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Agentic

Not comparable

Kimi K2.7 Code: 38.5; Supported · #69/128
Beam 1.0 501B-A23B FP8: Not ranked
Basis: BenchAlign v5.8 lane · 4 vs 6 public rows
Reading: Not comparable

Coding

Not comparable

Kimi K2.7 Code: 41.8; Supported · #62/150
Beam 1.0 501B-A23B FP8: Not ranked
Basis: BenchAlign v5.8 lane · 7 vs 7 public rows
Reading: Not comparable

Reasoning

Not comparable

Kimi K2.7 Code: 80.3; Unranked · 2 rankable rows
Beam 1.0 501B-A23B FP8: 85.8; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Kimi K2.7 Code: Not ranked
Beam 1.0 501B-A23B FP8: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Kimi K2.7 Code: 54.3; Estimated · #64/184
Beam 1.0 501B-A23B FP8: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

Kimi K2.7 Code: Not ranked
Beam 1.0 501B-A23B FP8: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Kimi K2.7 Code: 58.3; Unranked · 2 rankable rows
Beam 1.0 501B-A23B FP8: 87.9; Unranked · 1 rankable row
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Kimi K2.7 Code and Beam 1.0 501B-A23B FP8
Category	Kimi K2.7 Code	Beam 1.0 501B-A23B FP8	Basis	Reading
Instruction following	76.6#63/148	89.8#28/148	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Agentic	38.5Supported · #69/128	Not ranked	Not comparableBenchAlign v5.8 lane · 4 vs 6 public rows	Not comparable
Coding	41.8Supported · #62/150	Not ranked	Not comparableBenchAlign v5.8 lane · 7 vs 7 public rows	Not comparable
Reasoning	80.3Unranked · 2 rankable rows	85.8Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	54.3Estimated · #64/184	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 2 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	58.3Unranked · 2 rankable rows	87.9Unranked · 1 rankable row	Not comparableProvisional lane · 2 vs 1 weighted rows	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

Kimi K2.7 Code: $0.00295; 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

Kimi K2.7 Code: $0.0595; 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

Kimi K2.7 Code: $0.097; 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.

SpecificationKimi K2.7 CodeBeam 1.0 501B-A23B FP8

Context window

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

Kimi K2.7 Code

256K

Beam 1.0 501B-A23B FP8

Reflection Beam 1.0 FP8 model card

API model ID

Kimi K2.7 Code

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.

Kimi K2.7 Code

$0.19 per 1M cached input tokens

Beam 1.0 501B-A23B FP8

No comparable hosted API rate

Reflection Hugging Face model card

Documented inputs

Kimi K2.7 Code

Not sourced

Beam 1.0 501B-A23B FP8

text

Reflection Beam 1.0 FP8 model card

Documented outputs

Kimi K2.7 Code

Not sourced

Beam 1.0 501B-A23B FP8

text

Reflection Beam 1.0 FP8 model card

Provider availability

Kimi K2.7 Code

Not sourced

Beam 1.0 501B-A23B FP8

Open Weights · Hugging Face, self-hosted inference

Reflection Beam 1.0 FP8 model card

Reasoning profile

Kimi K2.7 Code

Reasoning

Beam 1.0 501B-A23B FP8

Reasoning

Weight access

Kimi K2.7 Code

Open Weight

Beam 1.0 501B-A23B FP8

Open Weight

License

Kimi K2.7 Code

Open Weight

Beam 1.0 501B-A23B FP8

Open Weight

Release date

Kimi K2.7 Code

2026-06-12

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: 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, Kimi K2.7 Code 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, Kimi K2.7 Code 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, Kimi K2.7 Code 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, Kimi K2.7 Code 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, Kimi K2.7 Code or Beam 1.0 501B-A23B FP8?

Beam 1.0 501B-A23B FP8 has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

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

Kimi K2.7 Code

API / mo$3,713

Self-host / mo$27,962

Break-even500M/day

Beam 1.0 501B-A23B FP8

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

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 evidence31 rows

Agentic

Kimi Claw 24/7
Kimi K2.7 Code46.9%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
MCP Atlas
Kimi K2.7 Code76%
Source
Beam 1.0 501B-A23B FP881.5%
Source
Beam 1.0 501B-A23B FP8 leads this result
MCP Mark Verified
Kimi K2.7 Code81.1%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K2.7 Code67.0%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
τ³-bench results
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP836.8%
Source
Not directly comparable
HLE w/ tools
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP851.1%
Source
Not directly comparable
Terminal-Bench 2.1
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP878.9%
Source
Not directly comparable
BrowseComp
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP876.7%
Source
Not directly comparable
DeepSearchQA
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP879.6%
Source
Not directly comparable

Coding

Kimi Code Bench v2
Kimi K2.7 Code62.0%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
ProgramBench
Kimi K2.7 Code53.6%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
MLS-Bench Lite
Kimi K2.7 Code35.1%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
CursorBench 3.2
Kimi K2.7 Code49.7%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
OpenHarmony Bench
Kimi K2.7 Code52.1%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
LiveCodeBench (Vals)
Kimi K2.7 Code82.1%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
SWE-bench (Vals)
Kimi K2.7 Code78.2%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
DeepSWE
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP843.0%
Source
Not directly comparable
SWE-bench Pro
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP865.9%
Source
Not directly comparable
Terminal-Bench 2.1
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP878.9%
Source
Not directly comparable
SWE-Atlas Codebase QnA
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP832.2%
Source
Not directly comparable
SWE Multilingual
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP878.1%
Source
Not directly comparable
SWE-bench Verified
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP880.2%
Source
Not directly comparable
SciCode
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP848.3%
Source
Not directly comparable

Reasoning

CritPt
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP817.4%
Source
Not directly comparable
LongBench v2
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP867.6%
Source
Not directly comparable

Knowledge

GPQA-D
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP890.3%
Source
Not directly comparable
HLE w/o tools
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP836%
Source
Not directly comparable

Instruction following

IFBench
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP880.7%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Kimi K2.7 Code54.035%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
FrontierMath v2 (Tier 4)
Kimi K2.7 Code12.195%
Source
Beam 1.0 501B-A23B FP8—
Not directly comparable
AIME26
Kimi K2.7 Code—
Beam 1.0 501B-A23B FP898.0%
Source
Not directly comparable

31 public results · 1 shared

Related comparisons

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Watch Kimi K2.7 Code vs Beam 1.0 501B-A23B FP8

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Last updated October 16, 2026

Kimi K2.7 Code vs Beam 1.0 501B-A23B FP8

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Instruction following

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Kimi K2.7 Code or Beam 1.0 501B-A23B FP8?

Which is better for coding, Kimi K2.7 Code or Beam 1.0 501B-A23B FP8?

Which is better for agentic tasks, Kimi K2.7 Code or Beam 1.0 501B-A23B FP8?

Which costs less, Kimi K2.7 Code or Beam 1.0 501B-A23B FP8?

Which has the larger context window, Kimi K2.7 Code or Beam 1.0 501B-A23B FP8?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Instruction following

Math

Related comparisons

Watch Kimi K2.7 Code vs Beam 1.0 501B-A23B FP8