Jobe Qwen3.5-4B (frozen) vs Kimi K3

Updated September 30, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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

Jobe Qwen3.5-4B (frozen)

MantisShrimpdev

—

Evidence status unavailable

90% interval unavailable

Model B

Kimi K3

Moonshot AI

72.12/100

Supported · Public rank #15

90% interval 68.8–75.4

Shared results: 0
Jobe Qwen3.5-4B (frozen) only: 1
Kimi K3 only: 48
Like-for-like categories: 0 / 8

Supported: Kimi K3How 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
Jobe Qwen3.5-4B (frozen) 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
Jobe Qwen3.5-4B (frozen) 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
Not enough matched evidence
A complete context comparison is not sourced.
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.

—Jobe Qwen3.5-4B (frozen)61.4Kimi K3

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.

Agentic

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: 67.8; Supported · #10/119
Basis: BenchAlign v5.8 lane · 0 vs 12 public rows
Reading: Not comparable

Coding

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: 61.4; Supported · #18/144
Basis: BenchAlign v5.8 lane · 0 vs 14 public rows
Reading: Not comparable

Reasoning

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: 65.8; #18/27
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: 89.4; #1/49
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: 67.8; Supported · #18/170
Basis: BenchAlign v5.8 lane · 0 vs 6 public rows
Reading: Not comparable

Multilingual

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Jobe Qwen3.5-4B (frozen): Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Jobe Qwen3.5-4B (frozen) and Kimi K3
Category	Jobe Qwen3.5-4B (frozen)	Kimi K3	Basis	Reading
Agentic	Not ranked	67.8Supported · #10/119	Not comparableBenchAlign v5.8 lane · 0 vs 12 public rows	Not comparable
Coding	Not ranked	61.4Supported · #18/144	Not comparableBenchAlign v5.8 lane · 0 vs 14 public rows	Not comparable
Reasoning	Not ranked	65.8#18/27	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	89.4#1/49	Not comparableProvisional lane · 0 vs 3 weighted rows	Not comparable
Knowledge	Not ranked	67.8Supported · #18/170	Not comparableBenchAlign v5.8 lane · 0 vs 6 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 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

Jobe Qwen3.5-4B (frozen): API rate not published; Fit state unavailable
Kimi K3: $0.0105; Fits in one request

Jobe Qwen3.5-4B (frozen) has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Jobe Qwen3.5-4B (frozen): API rate not published; Fit state unavailable
Kimi K3: $0.195; Fits in one request

Jobe Qwen3.5-4B (frozen) has no comparable published API token rate.

Cache-heavy agent loop

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

Jobe Qwen3.5-4B (frozen): API rate not published; Fit state unavailable; Cached-input rate unavailable
Kimi K3: $0.27; Fits in one request

Jobe Qwen3.5-4B (frozen) 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.

SpecificationJobe Qwen3.5-4B (frozen)Kimi K3

Context window

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

Jobe Qwen3.5-4B (frozen)

Not sourced

Kimi K3

1.05M

API model ID

Jobe Qwen3.5-4B (frozen)

Not sourced

Kimi K3

Not sourced

Cached-input rate

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

Jobe Qwen3.5-4B (frozen)

No comparable hosted API rate

Provider pricing

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Jobe Qwen3.5-4B (frozen)

Not sourced

Kimi K3

Not sourced

Documented outputs

Jobe Qwen3.5-4B (frozen)

Not sourced

Kimi K3

Not sourced

Provider availability

Jobe Qwen3.5-4B (frozen)

Not sourced

Kimi K3

Not sourced

Reasoning profile

Jobe Qwen3.5-4B (frozen)

Non-Reasoning

Kimi K3

Reasoning

Weight access

Jobe Qwen3.5-4B (frozen)

Open Weight

Kimi K3

Pending

License

Jobe Qwen3.5-4B (frozen)

Open Weight

Kimi K3

Pending

Release date

Jobe Qwen3.5-4B (frozen)

Not sourced

Kimi K3

2026-07-16

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: 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: A complete documented context comparison is not available.

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Jobe Qwen3.5-4B (frozen) or Kimi K3?

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, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Jobe Qwen3.5-4B (frozen) is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Jobe Qwen3.5-4B (frozen) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Jobe Qwen3.5-4B (frozen) or Kimi K3?

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, Jobe Qwen3.5-4B (frozen) or Kimi K3?

A complete documented context-window comparison is not available.

Benchmark evidence

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

Browse raw public benchmark evidence49 rows

Agentic

Terminal-Bench 2.1
Jobe Qwen3.5-4B (frozen)—
Kimi K388.3%
Source
Not directly comparable
BrowseComp
Jobe Qwen3.5-4B (frozen)—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
Jobe Qwen3.5-4B (frozen)—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
Jobe Qwen3.5-4B (frozen)—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
Jobe Qwen3.5-4B (frozen)—
Kimi K384.2%
Source
Not directly comparable
AutomationBench
Jobe Qwen3.5-4B (frozen)—
Kimi K330.8%
Source
Not directly comparable
JobBench
Jobe Qwen3.5-4B (frozen)—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
Jobe Qwen3.5-4B (frozen)—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
Jobe Qwen3.5-4B (frozen)—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
Jobe Qwen3.5-4B (frozen)—
Kimi K373.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Jobe Qwen3.5-4B (frozen)—
Kimi K380.9%
Source
Not directly comparable
ApprenticeBench
Jobe Qwen3.5-4B (frozen)—
Kimi K318%
Source
Not directly comparable

Coding

DeepSWE
Jobe Qwen3.5-4B (frozen)—
Kimi K367.5%
Source
Not directly comparable
cursorBench32
Jobe Qwen3.5-4B (frozen)—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
Jobe Qwen3.5-4B (frozen)—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
Jobe Qwen3.5-4B (frozen)—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
Jobe Qwen3.5-4B (frozen)—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
Jobe Qwen3.5-4B (frozen)—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
Jobe Qwen3.5-4B (frozen)—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
Jobe Qwen3.5-4B (frozen)—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
Jobe Qwen3.5-4B (frozen)—
Kimi K373.7%
Source
Not directly comparable
OpenHarmony Bench
Jobe Qwen3.5-4B (frozen)—
Kimi K357.3%
Source
Not directly comparable
FrontierSWE v2
Jobe Qwen3.5-4B (frozen)—
Kimi K325.9%
Source
Not directly comparable
LiveCodeBench (Vals)
Jobe Qwen3.5-4B (frozen)—
Kimi K387.2%
Source
Not directly comparable
SWE-bench (Vals)
Jobe Qwen3.5-4B (frozen)—
Kimi K393.4%
Source
Not directly comparable
PostTrainBench v1.1
Jobe Qwen3.5-4B (frozen)—
Kimi K332.0%
Source
Not directly comparable

Reasoning

JevBench 1.4
Jobe Qwen3.5-4B (frozen)46.94
Source
Kimi K3—
Not directly comparable
ARC-AGI-1
Jobe Qwen3.5-4B (frozen)—
Kimi K394.50%
Source
Not directly comparable
ARC-AGI-2
Jobe Qwen3.5-4B (frozen)—
Kimi K360.4%
Source
Not directly comparable

Multimodal

OfficeQA Pro
Jobe Qwen3.5-4B (frozen)—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
Jobe Qwen3.5-4B (frozen)—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
Jobe Qwen3.5-4B (frozen)—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
Jobe Qwen3.5-4B (frozen)—
Kimi K384.8%
Source
Not directly comparable
CharXiv
Jobe Qwen3.5-4B (frozen)—
Kimi K391.3%
Source
Not directly comparable
MathVision
Jobe Qwen3.5-4B (frozen)—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
Jobe Qwen3.5-4B (frozen)—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
Jobe Qwen3.5-4B (frozen)—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
Jobe Qwen3.5-4B (frozen)—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
Jobe Qwen3.5-4B (frozen)—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
Jobe Qwen3.5-4B (frozen)—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
Jobe Qwen3.5-4B (frozen)—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
Jobe Qwen3.5-4B (frozen)—
Kimi K358.5%
Source
Not directly comparable

Knowledge

GPQA
Jobe Qwen3.5-4B (frozen)—
Kimi K393.5%
Source
Not directly comparable
GPQA-D
Jobe Qwen3.5-4B (frozen)—
Kimi K393.5%
Source
Not directly comparable
HLE
Jobe Qwen3.5-4B (frozen)—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
Jobe Qwen3.5-4B (frozen)—
Kimi K343.5%
Source
Not directly comparable
GPQA Diamond (Vals)
Jobe Qwen3.5-4B (frozen)—
Kimi K392.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Jobe Qwen3.5-4B (frozen)—
Kimi K388.0%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Jobe Qwen3.5-4B (frozen)—
Kimi K352.7%
Source
Not directly comparable

49 public results · 0 shared

Related comparisons

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Last updated September 30, 2026

Jobe Qwen3.5-4B (frozen) vs Kimi K3

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

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

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

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, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Which is better for coding, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Which is better for agentic tasks, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Which costs less, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Which has the larger context window, Jobe Qwen3.5-4B (frozen) or Kimi K3?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch Jobe Qwen3.5-4B (frozen) vs Kimi K3