Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Ember-1 vs Kimi K3
Updated September 28, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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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. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Ember-1 only
- 3
- Kimi K3 only
- 44
- 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ember-1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Ember-1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
No clear pick
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.
—Ember-161.5Kimi K3
Not comparable · BenchAlign v5.7
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.
3 Ember-1 only
2 Shared
44 Kimi K3 only
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 v5.7 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- Ember-1
- Not ranked
- Kimi K3
- 69.0
- Supported · #7/111
- Basis
- BenchAlign v5.7 lane · 2 vs 12 public rows
- Reading
- Not comparable
Coding
Not comparable- Ember-1
- Not ranked
- Kimi K3
- 61.5
- Supported · #15/136
- Basis
- BenchAlign v5.7 lane · 3 vs 13 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Ember-1
- Not ranked
- Kimi K3
- 65.6
- #17/27
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ember-1
- Not ranked
- Kimi K3
- 89.4
- #1/50
- Basis
- Provisional lane · 0 vs 3 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Ember-1
- Not ranked
- Kimi K3
- 68.5
- Supported · #15/160
- Basis
- BenchAlign v5.7 lane · 0 vs 6 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Ember-1
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ember-1
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ember-1
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ember-1 | Kimi K3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 69.0Supported · #7/111 | Not comparableBenchAlign v5.7 lane · 2 vs 12 public rows | Not comparable |
| Coding | Not ranked | 61.5Supported · #15/136 | Not comparableBenchAlign v5.7 lane · 3 vs 13 public rows | Not comparable |
| Reasoning | Not ranked | 65.6#17/27 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 89.4#1/50 | Not comparableProvisional lane · 0 vs 3 weighted rows | Not comparable |
| Knowledge | Not ranked | 68.5Supported · #15/160 | Not comparableBenchAlign v5.7 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 v5.7) 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
- Ember-1
- $0.0105
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Ember-1
- $0.195
- Fits in one request
- Kimi K3
- $0.195
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ember-1
- $0.27
- Fits in one request
- Kimi K3
- $0.27
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
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.
SpecificationEmber-1Kimi K3
Context window
Maximum documented context; output-token limits may be lower.
Ember-1
Kimi K3
1.05M
API model ID
Ember-1
accounts/fireworks/models/ember-1
Fireworks Ember-1 model pageKimi K3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ember-1
$0.3 per 1M cached input tokens
Fireworks Ember-1 model pageKimi K3
$0.3 per 1M cached input tokens
Documented inputs
Ember-1
Not sourced
Kimi K3
Not sourced
Documented outputs
Ember-1
Not sourced
Kimi K3
Not sourced
Provider availability
Ember-1
Not sourced
Kimi K3
Not sourced
Reasoning profile
Ember-1
Reasoning
Kimi K3
Reasoning
Weight access
Ember-1
Proprietary
Kimi K3
Pending
License
Ember-1
Proprietary
Kimi K3
Pending
Release date
Ember-1
2026-09-23
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
- 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
- Repository review: $0.195 vs $0.195. Cache-heavy agent loop: $0.27 vs $0.27.
- Context tradeoff
- Kimi K3 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Ember-1 or Kimi K3?
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, Ember-1 or Kimi K3?
Ember-1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Ember-1 or Kimi K3?
Ember-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Ember-1 or Kimi K3?
For the stated presets, chat costs $0.0105 on Ember-1 and $0.0105 on Kimi K3; repository review costs $0.195 and $0.195; the cache-heavy agent loop costs $0.27 and $0.27. Costs use the listed standard API rates.
Which has the larger context window, Ember-1 or Kimi K3?
Kimi K3 has the larger documented context window: 1.05M, compared with 1.04M.
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
Ember-182.0%SourceKimi K388.3%SourceKimi K3 leads this result
τ²-bench Airline
Ember-166.0%SourceKimi K3—Not directly comparable
BrowseComp
Ember-1—Kimi K391.2%SourceNot directly comparable
DeepSearchQA
Ember-1—Kimi K395.0%SourceNot directly comparable
Toolathlon-Verified
Ember-1—Kimi K373.2%SourceNot directly comparable
MCP Atlas
Ember-1—Kimi K384.2%SourceNot directly comparable
AutomationBench
Ember-1—Kimi K330.8%SourceNot directly comparable
JobBench
Ember-1—Kimi K352.9%SourceNot directly comparable
APEX-Agents
Ember-1—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
Ember-1—Kimi K334.8%SourceNot directly comparable
DECK-Bench
Ember-1—Kimi K373.5%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Ember-1—Kimi K380.9%SourceNot directly comparable
ApprenticeBench
Ember-1—Kimi K318%SourceNot directly comparable
Coding
Terminal-Bench 2.1
Ember-182.0%SourceKimi K3—Not directly comparable
SWE-bench Verified
Ember-192.2%SourceKimi K3—Not directly comparable
DeepSWE
Ember-175.2%SourceKimi K367.5%SourceEmber-1 leads this result
cursorBench32
Ember-1—Kimi K360.8%SourceNot directly comparable
FrontierSWE
Ember-1—Kimi K381.2%SourceNot directly comparable
ProgramBench
Ember-1—Kimi K377.8%SourceNot directly comparable
Kimi Code Bench v2
Ember-1—Kimi K372.9%SourceNot directly comparable
sweMarathon
Ember-1—Kimi K342%SourceNot directly comparable
PostTrain Bench
Ember-1—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
Ember-1—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
Ember-1—Kimi K373.7%SourceNot directly comparable
OpenHarmony Bench
Ember-1—Kimi K357.3%SourceNot directly comparable
FrontierSWE v2
Ember-1—Kimi K325.9%SourceNot directly comparable
LiveCodeBench (Vals)
Ember-1—Kimi K387.2%SourceNot directly comparable
SWE-bench (Vals)
Ember-1—Kimi K393.4%SourceNot directly comparable
Reasoning
Multimodal
OfficeQA Pro
Ember-1—Kimi K363.3%SourceNot directly comparable
MMMU-Pro
Ember-1—Kimi K381.6%SourceNot directly comparable
MMMU-Pro w/ Python
Ember-1—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
Ember-1—Kimi K384.8%SourceNot directly comparable
CharXiv
Ember-1—Kimi K391.3%SourceNot directly comparable
MathVision
Ember-1—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
Ember-1—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
Ember-1—Kimi K385.7%SourceNot directly comparable
ZeroBench
Ember-1—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
Ember-1—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
Ember-1—Kimi K351.0%SourceNot directly comparable
OmniDocBench
Ember-1—Kimi K391.1%SourceNot directly comparable
PerceptionBench
Ember-1—Kimi K358.5%SourceNot directly comparable
Knowledge
GPQA
Ember-1—Kimi K393.5%SourceNot directly comparable
GPQA-D
Ember-1—Kimi K393.5%SourceNot directly comparable
HLE
Ember-1—Kimi K356%SourceNot directly comparable
HLE w/o tools
Ember-1—Kimi K343.5%SourceNot directly comparable
GPQA Diamond (Vals)
Ember-1—Kimi K392.9%SourceNot directly comparable
MMLU-Pro (Vals)
Ember-1—Kimi K388.0%SourceNot directly comparable
49 public results · 2 shared
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Last updated September 28, 2026