Long documents
Prompts that approach the documented context limit
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next has the larger documented context window.
Confidence: documented
Kimi K2 vs Qwen3.8-Flash-Next
Updated October 10, 2026. Rank says Qwen3.8-Flash-Next is ahead. Price, access, and your workload can each overturn that. 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
Share or export
Decision reading
Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 34.03. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- Kimi K2 only
- 11
- Qwen3.8-Flash-Next only
- 20
- Like-for-like categories
- 0 / 8
Estimated: Kimi K2 and Qwen3.8-Flash-Next. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K2 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
Kimi K2 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
Not enough matched evidence
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
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. Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Flash-Next has no comparable published API token rate.
Confidence: rate-fallbackRepository 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.
—Kimi K252.4Qwen3.8-Flash-Next
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.
11 Kimi K2 only
4 Shared
20 Qwen3.8-Flash-Next only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
SWE MultilingualCoding
Normalized gap 33.7Kimi K2:47.3%Qwen3.8-Flash-Next:81%HLEKnowledge
Normalized gap 31.2Kimi K2:4.7%Qwen3.8-Flash-Next:35.9%GPQAKnowledge
Normalized gap 16.6Kimi K2:75.1%Qwen3.8-Flash-Next:91.7%
Bars run 0–100 on each benchmark’s normalized display scale
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.
Knowledge
Directional only- Kimi K2
- 36.6
- Estimated · #123/177
- Qwen3.8-Flash-Next
- 59.4
- Supported · #47/177
- Basis
- BenchAlign v5.8 lane · 6 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- Kimi K2
- 48.4
- #85/127
- Qwen3.8-Flash-Next
- 91.2
- #22/127
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Kimi K2
- Not ranked
- Qwen3.8-Flash-Next
- 59.5
- Estimated · #24/123
- Basis
- BenchAlign v5.8 lane · 0 vs 6 public rows
- Reading
- Not comparable
Coding
Not comparable- Kimi K2
- Not ranked
- Qwen3.8-Flash-Next
- 52.4
- Supported · #37/146
- Basis
- BenchAlign v5.8 lane · 3 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Kimi K2
- 62.4
- Unranked · 2 rankable rows
- Qwen3.8-Flash-Next
- 80.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K2
- Not ranked
- Qwen3.8-Flash-Next
- 87.7
- #8/54
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K2
- Not ranked
- Qwen3.8-Flash-Next
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K2
- 39.5
- Unranked · 5 rankable rows
- Qwen3.8-Flash-Next
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Kimi K2 | Qwen3.8-Flash-Next | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 36.6Estimated · #123/177 | 59.4Supported · #47/177 | Directional onlyBenchAlign v5.8 lane · 6 vs 4 public rows | Directional only |
| Instruction following | 48.4#85/127 | 91.2#22/127 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | Not ranked | 59.5Estimated · #24/123 | Not comparableBenchAlign v5.8 lane · 0 vs 6 public rows | Not comparable |
| Coding | Not ranked | 52.4Supported · #37/146 | Not comparableBenchAlign v5.8 lane · 3 vs 5 public rows | Not comparable |
| Reasoning | 62.4Unranked · 2 rankable rows | 80.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 87.7#8/54 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 39.5Unranked · 5 rankable rows | Not ranked | Not comparableProvisional lane · 2 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
- Kimi K2
- $0.00185
- Fits in one request
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-Flash-Next has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Kimi K2
- $0.0375
- Fits in one request
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-Flash-Next has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Kimi K2
- $0.157
- Does not fit in one request
- Cached input priced at the published list-input rate
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Flash-Next 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 K2Qwen3.8-Flash-Next
Context window
Maximum documented context; output-token limits may be lower.
Kimi K2
128K
Qwen3.8-Flash-Next
API model ID
Kimi K2
Not sourced
Qwen3.8-Flash-Next
Qwen/Qwen3.8-Flash-Next
Qwen3.8-Flash-Next model cardCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2
Not published
Qwen3.8-Flash-Next
No comparable hosted API rate
Qwen3.8-Flash-Next model cardDocumented inputs
Kimi K2
Not sourced
Qwen3.8-Flash-Next
Not sourced
Documented outputs
Kimi K2
Not sourced
Qwen3.8-Flash-Next
Not sourced
Provider availability
Kimi K2
Not sourced
Qwen3.8-Flash-Next
Not sourced
Reasoning profile
Kimi K2
Non-Reasoning
Qwen3.8-Flash-Next
Reasoning
Weight access
Kimi K2
Proprietary
Qwen3.8-Flash-Next
Open Weight
License
Kimi K2
Proprietary
Qwen3.8-Flash-Next
Open Weight
Release date
Kimi K2
2025-07-01
Qwen3.8-Flash-Next
2026-08-26
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
- Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 34.03. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.8-Flash-Next has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Kimi K2 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 34.03. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Kimi K2 or Qwen3.8-Flash-Next?
Kimi K2 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Kimi K2 or Qwen3.8-Flash-Next?
Kimi K2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Kimi K2 or Qwen3.8-Flash-Next?
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 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next has the larger documented context window: 262K, 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 evidence35 rows
Agentic
CoWorkBench
Kimi K2—Qwen3.8-Flash-Next73.9%SourceNot directly comparable
JobBench
Kimi K2—Qwen3.8-Flash-Next55.7%SourceNot directly comparable
Agents' Last Exam
Kimi K2—Qwen3.8-Flash-Next51.2%SourceNot directly comparable
Toolathlon-Verified
Kimi K2—Qwen3.8-Flash-Next73.5%SourceNot directly comparable
AndroidWorld
Kimi K2—Qwen3.8-Flash-Next84.5%SourceNot directly comparable
OSWorld 2.0
Kimi K2—Qwen3.8-Flash-Next19.4%SourceNot directly comparable
Coding
SWE-bench Verified
Kimi K265.8%SourceQwen3.8-Flash-Next—Not directly comparable
LiveCodeBench v6
Kimi K253.7%SourceQwen3.8-Flash-Next91.9%SourceQwen3.8-Flash-Next leads this result
SWE Multilingual
Kimi K247.3%SourceQwen3.8-Flash-Next81%SourceQwen3.8-Flash-Next leads this result
SWE-bench Pro
Kimi K2—Qwen3.8-Flash-Next62.5%SourceNot directly comparable
NL2Repo
Kimi K2—Qwen3.8-Flash-Next48.1%SourceNot directly comparable
DeepSWE
Kimi K2—Qwen3.8-Flash-Next58.7%SourceNot directly comparable
Multimodal
Vision2Web
Kimi K2—Qwen3.8-Flash-Next64.0%SourceNot directly comparable
ERQA
Kimi K2—Qwen3.8-Flash-Next72.3%SourceNot directly comparable
LVBench
Kimi K2—Qwen3.8-Flash-Next76.6%SourceNot directly comparable
RealWorldQA
Kimi K2—Qwen3.8-Flash-Next88.5%SourceNot directly comparable
MathVision
Kimi K2—Qwen3.8-Flash-Next90.6%SourceNot directly comparable
MathVision w/ Python
Kimi K2—Qwen3.8-Flash-Next95.7%SourceNot directly comparable
CharXiv w/o tools
Kimi K2—Qwen3.8-Flash-Next84.6%SourceNot directly comparable
CharXiv
Kimi K2—Qwen3.8-Flash-Next90.6%SourceNot directly comparable
Knowledge
MMLU
Kimi K289.5%SourceQwen3.8-Flash-Next—Not directly comparable
GPQA
Kimi K275.1%SourceQwen3.8-Flash-Next91.7%SourceQwen3.8-Flash-Next leads this result
SuperGPQA
Kimi K257.2%SourceQwen3.8-Flash-Next—Not directly comparable
MMLU-Pro
Kimi K281.1%SourceQwen3.8-Flash-Next—Not directly comparable
SimpleQA
Kimi K231%SourceQwen3.8-Flash-Next—Not directly comparable
HLE
Kimi K24.7%SourceQwen3.8-Flash-Next35.9%SourceQwen3.8-Flash-Next leads this result
GPQA-D
Kimi K2—Qwen3.8-Flash-Next91.7%SourceNot directly comparable
HLE w/o tools
Kimi K2—Qwen3.8-Flash-Next35.9%SourceNot directly comparable
Instruction following
Math
AIME 2024
Kimi K269.6%SourceQwen3.8-Flash-Next—Not directly comparable
AIME 2025
Kimi K249.5%SourceQwen3.8-Flash-Next—Not directly comparable
MATH-500
Kimi K297.4%SourceQwen3.8-Flash-Next—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Kimi K221.404%SourceQwen3.8-Flash-Next—Not directly comparable
FrontierMath v2 (Tier 4)
Kimi K20.000%SourceQwen3.8-Flash-Next—Not directly comparable
35 public results · 4 shared
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Last updated October 10, 2026