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Model Comparison

Claude Haiku 4.5 vs Ling 3.0 Flash VLWhich Is Better in 2026?

Anthropic: Claude Haiku 4.5 logoAnthropic•
inclusionAI

Claude Haiku 4.5 vs Ling 3.0 Flash VL: which should you choose in 2026?

Claude Haiku 4.5 (by Anthropic) and Ling 3.0 Flash VL (by inclusionAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Ling 3.0 Flash VL is about 22.2× cheaper on a blended 3:1 input/output basis ($0.09 vs $2.00 per 1M tokens).

Claude Haiku 4.5 has the larger context window (200,000 tokens vs 131,072 tokens).

Choose Claude Haiku 4.5 if…

  • • you work with longer documents, transcripts, or codebases

Choose Ling 3.0 Flash VL if…

  • • lower blended API cost matters for your workload

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Claude Haiku 4.5 and Ling 3.0 Flash VL benchmark results
BenchmarkClaude Haiku 4.5Ling 3.0 Flash VL
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

——
BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

——
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

——
SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

66.6%
SWE-bench
—
Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

——
AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

——
GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

——
Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

——

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

Claude Haiku 4.5 and Ling 3.0 Flash VL model facts
FeatureClaude Haiku 4.5Ling 3.0 Flash VL
Context & model facts
DeveloperAnthropicinclusionAI
API providerAnthropicinclusionAI
Input context200,000 tokens131,072 tokens
Maximum output64,000 tokens32,768 tokens
Released——
Added to WritingmateOct 15, 2025Sep 10, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsText, Image, FileText, Image, Video
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$1.00$0.06
Output (per 1M tokens)$5.00$0.18
Blended 3:1 input/output$2.00$0.09
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated Sep 10, 2026.

Why Pay for Multiple Subscriptions?

Comparing Claude Haiku 4.5 from Anthropic with Ling 3.0 Flash VL from inclusionAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Claude Haiku 4.5 and Ling 3.0 Flash VL
PlanPriceClaude Haiku 4.5Ling 3.0 Flash VLAI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

Claude Haiku 4.5 vs Ling 3.0 Flash VL FAQ

Which is better, Claude Haiku 4.5 or Ling 3.0 Flash VL?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.0 Flash VL is about 22.2× cheaper on a blended 3:1 input/output basis ($0.09 vs $2.00 per 1M tokens). Claude Haiku 4.5 has the larger context window (200,000 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

Ling 3.0 Flash VL is about 22.2× cheaper on a blended 3:1 input/output basis ($0.09 vs $2.00 per 1M tokens).

Which model supports more context?

Claude Haiku 4.5 has the larger context window (200,000 tokens vs 131,072 tokens).

Can I switch between Claude Haiku 4.5 and Ling 3.0 Flash VL?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.