Agentic
Like-for-like- Claude Haiku 4.5
- 27.0
- Supported · #143/151
- Ling 3.0 Flash
- 40.0
- Supported · #121/151
- Basis
- BenchAlign lane · 2 vs 7 public rows
- Reading
- Ling 3.0 Flash leads · intervals overlap
Claude Haiku 4.5 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
Claude Haiku 4.5 has the higher public score estimate, 52.73 versus 52.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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.
Agentic work
Tool use, computer use, and multi-step task completion
Ling 3.0 Flash
Ling 3.0 Flash leads on the public agentic lane, 40 to 27, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
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
Ling 3.0 Flash is scored on Estimated evidence for coding, 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Haiku 4.5 does not fit this workload in one request. Ling 3.0 Flash has no comparable published API token rate.
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 Claude Haiku 4.5 only
5 Shared
17 Ling 3.0 Flash only
- Shared results
- 5
- Claude Haiku 4.5 only
- 5
- Ling 3.0 Flash only
- 17
- Like-for-like categories
- 1 / 8
2 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- Claude Haiku 4.5
- 27.3
- Supported · #173/183
- Ling 3.0 Flash
- 42.8
- Estimated · #126/183
- Basis
- BenchAlign lane · 4 vs 6 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Haiku 4.5
- 44.6
- Estimated · #117/181
- Ling 3.0 Flash
- 45.9
- Supported · #112/181
- Basis
- BenchAlign lane · 2 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Haiku 4.5
- Not ranked
- Ling 3.0 Flash
- 69.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Haiku 4.5
- 28.9
- Unranked · 2 rankable rows
- Ling 3.0 Flash
- 73.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Haiku 4.5
- Not ranked
- Ling 3.0 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Haiku 4.5
- Not ranked
- Ling 3.0 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Haiku 4.5
- Not ranked
- Ling 3.0 Flash
- 75.6
- #58/120
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Claude Haiku 4.5 | Ling 3.0 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 27.0Supported · #143/151 | 40.0Supported · #121/151 | Like-for-likeBenchAlign lane · 2 vs 7 public rows | Ling 3.0 Flash leads · intervals overlap |
| Coding | 27.3Supported · #173/183 | 42.8Estimated · #126/183 | Directional onlyBenchAlign lane · 4 vs 6 public rows | Directional only |
| Knowledge | 44.6Estimated · #117/181 | 45.9Supported · #112/181 | Directional onlyBenchAlign lane · 2 vs 5 public rows | Directional only |
| Reasoning | Not ranked | 69.2Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 28.9Unranked · 2 rankable rows | 73.7Unranked · 3 rankable rows | Not comparableProvisional lane · 2 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 |
| Instruction following | Not ranked | 75.6#58/120 | Not comparableProvisional lane · 0 vs 1 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.
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.
LiveCodeBench (Vals)
Coding
Claude Haiku 4.5: 41.2%Ling 3.0 Flash: 84.0%Normalized gap 42.8Claude Haiku 4.5 source Ling 3.0 Flash sourceMMLU-Pro (Vals)
Knowledge
Claude Haiku 4.5: 78.7%Ling 3.0 Flash: 82.0%Normalized gap 3.3Claude Haiku 4.5 source Ling 3.0 Flash source
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
- Claude Haiku 4.5
- $0.0035
- 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
- Claude Haiku 4.5
- $0.065
- 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
- Claude Haiku 4.5
- $0.09
- Does not fit in one request
- Ling 3.0 Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Claude Haiku 4.5 does not fit this workload in one request. 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.
SpecificationClaude Haiku 4.5Ling 3.0 Flash
Context window
Maximum documented context; output-token limits may be lower.
Claude Haiku 4.5
Ling 3.0 Flash
API model ID
Claude Haiku 4.5
claude-haiku-4-5-20251001
Claude API pricingLing 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.
Claude Haiku 4.5
$0.1 per 1M cached input tokens
Claude API pricingLing 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardDocumented inputs
Claude Haiku 4.5
Not sourced
Ling 3.0 Flash
Not sourced
Documented outputs
Claude Haiku 4.5
Not sourced
Ling 3.0 Flash
Not sourced
Provider availability
Claude Haiku 4.5
Not sourced
Ling 3.0 Flash
Not sourced
Reasoning profile
Claude Haiku 4.5
Non-Reasoning
Ling 3.0 Flash
Reasoning
Weight access
Claude Haiku 4.5
Proprietary
Ling 3.0 Flash
Open Weight
License
Claude Haiku 4.5
Proprietary
Ling 3.0 Flash
Open Weight
Release date
Claude Haiku 4.5
2025-10-15
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
- Claude Haiku 4.5 has the higher public score estimate, 52.73 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence27 rows
Agentic
JobBench
Claude Haiku 4.516.0%SourceLing 3.0 Flash—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Haiku 4.543.8%SourceLing 3.0 Flash50.2%SourceLing 3.0 Flash leads this result
MCP Atlas
Claude Haiku 4.5—Ling 3.0 Flash65.5%SourceNot directly comparable
skillsBench
Claude Haiku 4.5—Ling 3.0 Flash44.8%SourceNot directly comparable
BFCL v4
Claude Haiku 4.5—Ling 3.0 Flash73.0%SourceNot directly comparable
WideResearch
Claude Haiku 4.5—Ling 3.0 Flash73.6%SourceNot directly comparable
BrowseComp
Claude Haiku 4.5—Ling 3.0 Flash72.2%SourceNot directly comparable
DRACO
Claude Haiku 4.5—Ling 3.0 Flash70.4%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Haiku 4.573.3%SourceLing 3.0 Flash—Not directly comparable
VulcanBench v3
Claude Haiku 4.576.2%SourceLing 3.0 Flash—Not directly comparable
LiveCodeBench (Vals)
Claude Haiku 4.541.2%SourceLing 3.0 Flash84.0%SourceLing 3.0 Flash leads this result
SWE-bench (Vals)
Claude Haiku 4.566.6%SourceLing 3.0 Flash65.2%SourceClaude Haiku 4.5 leads this result
SWE-bench Pro
Claude Haiku 4.5—Ling 3.0 Flash56.6%SourceNot directly comparable
SWE Multilingual
Claude Haiku 4.5—Ling 3.0 Flash72.4%SourceNot directly comparable
LiveCodeBench v5
Claude Haiku 4.5—Ling 3.0 Flash82.8%SourceNot directly comparable
SciCode
Claude Haiku 4.5—Ling 3.0 Flash41.2%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Claude Haiku 4.572.2%SourceLing 3.0 Flash84.8%SourceLing 3.0 Flash leads this result
MMLU-Pro (Vals)
Claude Haiku 4.578.7%SourceLing 3.0 Flash82.0%SourceLing 3.0 Flash leads this result
GPQA
Claude Haiku 4.5—Ling 3.0 Flash85.0%SourceNot directly comparable
GPQA-D
Claude Haiku 4.5—Ling 3.0 Flash85.0%SourceNot directly comparable
HLE
Claude Haiku 4.5—Ling 3.0 Flash22.7%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Claude Haiku 4.55.903%SourceLing 3.0 Flash—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Haiku 4.52.083%SourceLing 3.0 Flash—Not directly comparable
AIME26
Claude Haiku 4.5—Ling 3.0 Flash93.2%SourceNot directly comparable
HMMT Feb 2026
Claude Haiku 4.5—Ling 3.0 Flash87.0%SourceNot directly comparable
IMOAnswerBench
Claude Haiku 4.5—Ling 3.0 Flash83.7%SourceNot directly comparable
Instruction following
IFBench
Claude Haiku 4.5—Ling 3.0 Flash74.5%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Haiku 4.5 or Ling 3.0 Flash?
Claude Haiku 4.5 has the higher public score estimate, 52.73 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, Claude Haiku 4.5 or Ling 3.0 Flash?
Ling 3.0 Flash scores higher for coding on the public lane, 42.8 to 27.3. Ling 3.0 Flash is 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, Claude Haiku 4.5 or Ling 3.0 Flash?
Ling 3.0 Flash leads the public agentic tasks lane, 40 to 27, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Haiku 4.5 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, Claude Haiku 4.5 or Ling 3.0 Flash?
Ling 3.0 Flash has the larger documented context window: 262K, compared with 200K.
Related comparisons
Last updated September 4, 2026
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