Agentic
Directional only- Kimi K2.7 Code
- 46.2
- Estimated · #86/151
- Ling 3.0 Flash
- 40.0
- Supported · #121/151
- Basis
- BenchAlign lane · 3 vs 7 public rows
- Reading
- Directional only
Kimi K2.7 Code vs Ling 3.0 Flash
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K2.7 Code has the higher public score estimate, 65.4 versus 52.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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
Ling 3.0 Flash
Ling 3.0 Flash 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
Kimi K2.7 Code and Ling 3.0 Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2.7 Code is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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: rate-fallback
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Kimi K2.7 Code only
1 Shared
21 Ling 3.0 Flash only
- Shared results
- 1
- Kimi K2.7 Code only
- 7
- Ling 3.0 Flash only
- 21
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Directional only- Kimi K2.7 Code
- 51.7
- Estimated · #61/183
- Ling 3.0 Flash
- 42.8
- Estimated · #126/183
- Basis
- BenchAlign lane · 5 vs 6 public rows
- Reading
- Directional only
Knowledge
Directional only- Kimi K2.7 Code
- 61.8
- Estimated · #32/181
- Ling 3.0 Flash
- 45.9
- Supported · #112/181
- Basis
- BenchAlign lane · 0 vs 5 public rows
- Reading
- Directional only
Instruction following
Directional only- Kimi K2.7 Code
- 76.3
- #56/120
- Ling 3.0 Flash
- 75.6
- #58/120
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Kimi K2.7 Code
- 74.9
- Unranked · 2 rankable rows
- Ling 3.0 Flash
- 69.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K2.7 Code
- Not ranked
- Ling 3.0 Flash
- 73.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K2.7 Code
- Not ranked
- Ling 3.0 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K2.7 Code
- Not ranked
- Ling 3.0 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Kimi K2.7 Code | Ling 3.0 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.2Estimated · #86/151 | 40.0Supported · #121/151 | Directional onlyBenchAlign lane · 3 vs 7 public rows | Directional only |
| Coding | 51.7Estimated · #61/183 | 42.8Estimated · #126/183 | Directional onlyBenchAlign lane · 5 vs 6 public rows | Directional only |
| Knowledge | 61.8Estimated · #32/181 | 45.9Supported · #112/181 | Directional onlyBenchAlign lane · 0 vs 5 public rows | Directional only |
| Instruction following | 76.3#56/120 | 75.6#58/120 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 74.9Unranked · 2 rankable rows | 69.2Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 73.7Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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 K2.7 Code
- $0.00295
- Fits in one request
- Ling 3.0 Flash
- API rate not published
- Fits in one request
Ling 3.0 Flash 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
- Ling 3.0 Flash
- API rate not published
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Kimi K2.7 Code
- $0.249
- Fits in one request
- Cached input priced at the published list-input rate
- Ling 3.0 Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Ling 3.0 Flash has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationKimi K2.7 CodeLing 3.0 Flash
Context window
Maximum documented context; output-token limits may be lower.
Kimi K2.7 Code
256K
Ling 3.0 Flash
API model ID
Kimi K2.7 Code
Not sourced
Ling 3.0 Flash
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
Not published
Ling 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardDocumented inputs
Kimi K2.7 Code
Not sourced
Ling 3.0 Flash
Not sourced
Documented outputs
Kimi K2.7 Code
Not sourced
Ling 3.0 Flash
Not sourced
Provider availability
Kimi K2.7 Code
Not sourced
Ling 3.0 Flash
Not sourced
Reasoning profile
Kimi K2.7 Code
Reasoning
Ling 3.0 Flash
Reasoning
Weight access
Kimi K2.7 Code
Open Weight
Ling 3.0 Flash
Open Weight
License
Kimi K2.7 Code
Open Weight
Ling 3.0 Flash
Open Weight
Release date
Kimi K2.7 Code
2026-06-12
Ling 3.0 Flash
2026-07-23
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
- Kimi K2.7 Code has the higher public score estimate, 65.4 versus 52.2, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Ling 3.0 Flash has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
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$18,221
Break-even326M/day
Ling 3.0 Flash
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
Kimi Claw 24/7
Kimi K2.7 Code46.9%SourceLing 3.0 Flash—Not directly comparable
MCP Atlas
Kimi K2.7 Code76%SourceLing 3.0 Flash65.5%SourceKimi K2.7 Code leads this result
MCP Mark Verified
Kimi K2.7 Code81.1%SourceLing 3.0 Flash—Not directly comparable
skillsBench
Kimi K2.7 Code—Ling 3.0 Flash44.8%SourceNot directly comparable
BFCL v4
Kimi K2.7 Code—Ling 3.0 Flash73.0%SourceNot directly comparable
WideResearch
Kimi K2.7 Code—Ling 3.0 Flash73.6%SourceNot directly comparable
BrowseComp
Kimi K2.7 Code—Ling 3.0 Flash72.2%SourceNot directly comparable
DRACO
Kimi K2.7 Code—Ling 3.0 Flash70.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K2.7 Code—Ling 3.0 Flash50.2%SourceNot directly comparable
Coding
Kimi Code Bench v2
Kimi K2.7 Code62.0%SourceLing 3.0 Flash—Not directly comparable
ProgramBench
Kimi K2.7 Code53.6%SourceLing 3.0 Flash—Not directly comparable
MLS-Bench Lite
Kimi K2.7 Code35.1%SourceLing 3.0 Flash—Not directly comparable
cursorBench32
Kimi K2.7 Code49.7%SourceLing 3.0 Flash—Not directly comparable
OpenHarmony Bench
Kimi K2.7 Code52.1%SourceLing 3.0 Flash—Not directly comparable
SWE-bench Pro
Kimi K2.7 Code—Ling 3.0 Flash56.6%SourceNot directly comparable
SWE Multilingual
Kimi K2.7 Code—Ling 3.0 Flash72.4%SourceNot directly comparable
LiveCodeBench v5
Kimi K2.7 Code—Ling 3.0 Flash82.8%SourceNot directly comparable
SciCode
Kimi K2.7 Code—Ling 3.0 Flash41.2%SourceNot directly comparable
LiveCodeBench (Vals)
Kimi K2.7 Code—Ling 3.0 Flash84.0%SourceNot directly comparable
SWE-bench (Vals)
Kimi K2.7 Code—Ling 3.0 Flash65.2%SourceNot directly comparable
Knowledge
GPQA
Kimi K2.7 Code—Ling 3.0 Flash85.0%SourceNot directly comparable
GPQA-D
Kimi K2.7 Code—Ling 3.0 Flash85.0%SourceNot directly comparable
HLE
Kimi K2.7 Code—Ling 3.0 Flash22.7%SourceNot directly comparable
GPQA Diamond (Vals)
Kimi K2.7 Code—Ling 3.0 Flash84.8%SourceNot directly comparable
MMLU-Pro (Vals)
Kimi K2.7 Code—Ling 3.0 Flash82.0%SourceNot directly comparable
Math
Instruction following
IFBench
Kimi K2.7 Code—Ling 3.0 Flash74.5%SourceNot directly comparable
Frequently asked questions
Which is better, Kimi K2.7 Code or Ling 3.0 Flash?
Kimi K2.7 Code has the higher public score estimate, 65.4 versus 52.2, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Kimi K2.7 Code or Ling 3.0 Flash?
Kimi K2.7 Code scores higher for coding on the public lane, 51.7 to 42.8. Kimi K2.7 Code and Ling 3.0 Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Kimi K2.7 Code or Ling 3.0 Flash?
Kimi K2.7 Code scores higher for agentic tasks on the public lane, 46.2 to 40. Kimi K2.7 Code is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Kimi K2.7 Code or Ling 3.0 Flash?
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 Ling 3.0 Flash?
Ling 3.0 Flash has the larger documented context window: 262K, compared with 256K.
Related comparisons
Last updated September 4, 2026
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