Model A

Moonshot AI

80.5/100

Supported · Public rank #5

90% interval 77.6–83.4

Kimi K3 vs Toast 1

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Toast 1

Mixedbread

—

Evidence status unavailable

90% interval unavailable

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 based on different benchmark sets are marked directional and do not name a winner.

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
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Toast 1
Toast 1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
Toast 1
Toast 1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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. Toast 1 does not fit this workload in one request.
Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

40 Kimi K3 only

Shared results: 0
Kimi K3 only: 40
Toast 1 only: 0
Like-for-like categories: 0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable

Kimi K3: 89.5
Toast 1: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Coding

Not comparable

Kimi K3: Not measured
Toast 1: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Reasoning

Not comparable

Kimi K3: Not measured
Toast 1: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

Kimi K3: 61.0
Toast 1: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Math

Not comparable

Kimi K3: Not measured
Toast 1: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Kimi K3: Not measured
Toast 1: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Kimi K3: 78.5
Toast 1: Not measured
Weighted basis: 3 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Kimi K3: Not measured
Toast 1: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Kimi K3 and Toast 1
Category	Kimi K3	Toast 1	Weighted basis	Reading
Agentic	89.5	Not measured	Not comparable2 vs 0 rows	Not comparable
Coding	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Knowledge	61.0	Not measured	Not comparable2 vs 0 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	78.5	Not measured	Not comparable3 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

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.

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 K3: $0.0105; Fits in one request
Toast 1: $0.00066; Fits in one request

Toast 1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Kimi K3: $0.195; Fits in one request
Toast 1: $0.01716; Fits in one request

Toast 1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Kimi K3: $0.27; Fits in one request
Toast 1: $0.0204; Does not fit in one request

Toast 1 does not fit this workload in one request.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationKimi K3Toast 1

Context window

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

Kimi K3

1.05M

Toast 1

131K

Mixedbread Agent model catalog

API model ID

Kimi K3

Not sourced

Toast 1

toast-1

Mixedbread Agent model catalog

Cached-input rate

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

Kimi K3

$0.3 per 1M cached input tokens

Toast 1

$0.036 per 1M cached input tokens

Mixedbread pricing

Documented inputs

Kimi K3

Not sourced

Toast 1

text

Mixedbread Agent model catalog

Documented outputs

Kimi K3

Not sourced

Toast 1

text

Mixedbread Agent model catalog

Provider availability

Kimi K3

Not sourced

Toast 1

Generally Available · Mixedbread Chat Completions API

Mixedbread Toast 1 announcement

Reasoning profile

Kimi K3

Reasoning

Toast 1

Reasoning

Weight access

Kimi K3

Pending

Toast 1

Proprietary

License

Kimi K3

Pending

Toast 1

Proprietary

Release date

Kimi K3

2026-07-16

Toast 1

2026-08-13

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: Repository review: $0.195 vs $0.01716. Cache-heavy agent loop: $0.27 vs $0.0204.
Context tradeoff: Kimi K3 has the larger documented window (1.05M).

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

Benchmark evidence

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

Browse raw public benchmark evidence40 rows

Agentic

Terminal-Bench 2.0
Kimi K388.3%
Source
Toast 1—
Not directly comparable
BrowseComp
Kimi K391.2%
Source
Toast 1—
Not directly comparable
DeepSearchQA
Kimi K395.0%
Source
Toast 1—
Not directly comparable
Toolathlon-Verified
Kimi K373.2%
Source
Toast 1—
Not directly comparable
MCP Atlas
Kimi K384.2%
Source
Toast 1—
Not directly comparable
AutomationBench
Kimi K330.8%
Source
Toast 1—
Not directly comparable
JobBench
Kimi K352.9%
Source
Toast 1—
Not directly comparable
APEX-Agents
Kimi K337.6%
Source
Toast 1—
Not directly comparable
SpreadsheetBench 2
Kimi K334.8%
Source
Toast 1—
Not directly comparable
DECK-Bench
Kimi K373.5%
Source
Toast 1—
Not directly comparable

Coding

deepSwe
Kimi K367.5%
Source
Toast 1—
Not directly comparable
cursorBench32
Kimi K360.8%
Source
Toast 1—
Not directly comparable
FrontierSWE
Kimi K381.2%
Source
Toast 1—
Not directly comparable
ProgramBench
Kimi K377.8%
Source
Toast 1—
Not directly comparable
Kimi Code Bench v2
Kimi K372.9%
Source
Toast 1—
Not directly comparable
sweMarathon
Kimi K342%
Source
Toast 1—
Not directly comparable
PostTrain Bench
Kimi K336.6%
Source
Toast 1—
Not directly comparable
MLS-Bench Lite
Kimi K348.3%
Source
Toast 1—
Not directly comparable
VulcanBench v3
Kimi K373.9%
Source
Toast 1—
Not directly comparable
APEX-SWE
Kimi K348.0%
Source
Toast 1—
Not directly comparable
EEBench
Kimi K338.3%
Source
Toast 1—
Not directly comparable
InferenceEval
Kimi K339.8%
Source
Toast 1—
Not directly comparable
KernelBench Internal
Kimi K368.5%
Source
Toast 1—
Not directly comparable

Knowledge

GPQA
Kimi K393.5%
Source
Toast 1—
Not directly comparable
GPQA-D
Kimi K393.5%
Source
Toast 1—
Not directly comparable
HLE
Kimi K356%
Source
Toast 1—
Not directly comparable
HLE w/o tools
Kimi K343.5%
Source
Toast 1—
Not directly comparable

Multimodal

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

Frequently asked questions

Which is better, Kimi K3 or Toast 1?

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, Kimi K3 or Toast 1?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Kimi K3 or Toast 1?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Kimi K3 or Toast 1?

For the stated presets, chat costs $0.0105 on Kimi K3 and $0.00066 on Toast 1; repository review costs $0.195 and $0.01716; the cache-heavy agent loop costs $0.27 and $0.0204. Toast 1 does not fit this workload in one request.

Which has the larger context window, Kimi K3 or Toast 1?

Kimi K3 has the larger documented context window: 1.05M, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

Watch Kimi K3 vs Toast 1

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

Read a sample issue

Join 2,000+ readers.

Kimi K3 vs Toast 1

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Knowledge

Multimodal

Frequently asked questions

Which is better, Kimi K3 or Toast 1?

Which is better for coding, Kimi K3 or Toast 1?

Which is better for agentic tasks, Kimi K3 or Toast 1?

Which costs less, Kimi K3 or Toast 1?

Which has the larger context window, Kimi K3 or Toast 1?

Related comparisons

Watch Kimi K3 vs Toast 1