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BenchLM
Anthropic logo
Model A
Claude 3 Haiku

Anthropic

17.34/100

Supported · Public rank #234

90% interval 9.8–24.9

Claude 3 Haiku vs Ling 3.0 Flash

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

InclusionAI logo
Model B
Ling 3.0 Flash

InclusionAI

52.2/100

Estimated · Public rank #124

90% interval 40.7–63.7

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Claude 3 Haiku 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

    Claude 3 Haiku is not ranked on the public lane for agentic, so no winner is named for agentic.

    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 3 Haiku does not fit this workload in one request. Claude 3 Haiku 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.

    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.

Shared results
0
Claude 3 Haiku only
0
Ling 3.0 Flash only
22
Like-for-like categories
0 / 8

3 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 3 Haiku
22.5
Estimated · #181/183
Ling 3.0 Flash
42.8
Estimated · #126/183
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Directional only

Knowledge

Directional only
Claude 3 Haiku
24.9
Estimated · #178/181
Ling 3.0 Flash
45.9
Supported · #112/181
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Directional only

Instruction following

Directional only
Claude 3 Haiku
41.3
#101/120
Ling 3.0 Flash
75.6
#58/120
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Agentic

Not comparable
Claude 3 Haiku
Not ranked
Ling 3.0 Flash
40.0
Supported · #121/151
Basis
BenchAlign lane · 0 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude 3 Haiku
39.0
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
Claude 3 Haiku
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
Claude 3 Haiku
Not ranked
Ling 3.0 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude 3 Haiku
25.3
Unranked · 1 rankable row
Ling 3.0 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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

Claude 3 Haiku
$0.00088
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 3 Haiku
$0.01625
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 3 Haiku
$0.0675
Does not fit 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

Claude 3 Haiku does not fit this workload in one request. Claude 3 Haiku 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.

API model ID

Claude 3 Haiku

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.

Claude 3 Haiku

Not published

Ling 3.0 Flash

No comparable hosted API rate

InclusionAI Ling 3.0 Flash model card

Documented inputs

Claude 3 Haiku

Not sourced

Ling 3.0 Flash

Not sourced

Documented outputs

Claude 3 Haiku

Not sourced

Ling 3.0 Flash

Not sourced

Provider availability

Claude 3 Haiku

Not sourced

Ling 3.0 Flash

Not sourced

Reasoning profile

Claude 3 Haiku

Non-Reasoning

Ling 3.0 Flash

Reasoning

Weight access

Claude 3 Haiku

Proprietary

Ling 3.0 Flash

Open Weight

License

Claude 3 Haiku

Proprietary

Ling 3.0 Flash

Open Weight

Release date

Claude 3 Haiku

2024-03-01

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
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 evidence22 rows

Agentic

  • MCP Atlas

    Claude 3 Haiku—
    Ling 3.0 Flash65.5%
    Source

    Not directly comparable

  • skillsBench

    Claude 3 Haiku—
    Ling 3.0 Flash44.8%
    Source

    Not directly comparable

  • BFCL v4

    Claude 3 Haiku—
    Ling 3.0 Flash73.0%
    Source

    Not directly comparable

  • WideResearch

    Claude 3 Haiku—
    Ling 3.0 Flash73.6%
    Source

    Not directly comparable

  • BrowseComp

    Claude 3 Haiku—
    Ling 3.0 Flash72.2%
    Source

    Not directly comparable

  • DRACO

    Claude 3 Haiku—
    Ling 3.0 Flash70.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude 3 Haiku—
    Ling 3.0 Flash50.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Claude 3 Haiku—
    Ling 3.0 Flash56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude 3 Haiku—
    Ling 3.0 Flash72.4%
    Source

    Not directly comparable

  • LiveCodeBench v5

    Claude 3 Haiku—
    Ling 3.0 Flash82.8%
    Source

    Not directly comparable

  • SciCode

    Claude 3 Haiku—
    Ling 3.0 Flash41.2%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude 3 Haiku—
    Ling 3.0 Flash84.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Claude 3 Haiku—
    Ling 3.0 Flash65.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude 3 Haiku—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • GPQA-D

    Claude 3 Haiku—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • HLE

    Claude 3 Haiku—
    Ling 3.0 Flash22.7%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude 3 Haiku—
    Ling 3.0 Flash84.8%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude 3 Haiku—
    Ling 3.0 Flash82.0%
    Source

    Not directly comparable

Math

  • AIME26

    Claude 3 Haiku—
    Ling 3.0 Flash93.2%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude 3 Haiku—
    Ling 3.0 Flash87.0%
    Source

    Not directly comparable

  • IMOAnswerBench

    Claude 3 Haiku—
    Ling 3.0 Flash83.7%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Claude 3 Haiku—
    Ling 3.0 Flash74.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude 3 Haiku or Ling 3.0 Flash?

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, Claude 3 Haiku or Ling 3.0 Flash?

Ling 3.0 Flash scores higher for coding on the public lane, 42.8 to 22.5. Claude 3 Haiku 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, Claude 3 Haiku or Ling 3.0 Flash?

Claude 3 Haiku is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude 3 Haiku 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 3 Haiku or Ling 3.0 Flash?

Ling 3.0 Flash has the larger documented context window: 262K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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