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Claude 3.5 Haiku vs Ling 3.0 Flash Fin

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 5.3. Ling 3.0 Flash Fin is 17.8x cheaper per token.

Anthropic · InclusionAI · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 5.3, ranking #39 overall.

On price, Ling 3.0 Flash Fin is roughly 17.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Ling 3.0 Flash Fin also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Claude 3.5 Haiku

  • you want predictable pricing at $0.80/M input and $4.00/M output

Choose Ling 3.0 Flash Fin

  • overall performance matters — it scores 43.3 and ranks #39 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • cost matters — it's about 17.8x cheaper per token
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

Core performance indexes
5.3
#296
43.3
#39
5.4
#292
44.7
#32
Cost, coverage & limits
Benchmark wins
Input price
$0.80 / M
$0.06 / M
Output price
$4.00 / M
$0.18 / M
Context window
200,000
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Claude 3.5 Haiku
Ling 3.0 Flash Fin
-4.5#188
20.5#56
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

9 reported for Claude 3.5 Haiku · 6 for Ling 3.0 Flash Fin

No common benchmarks found

Claude 3.5 Haiku and Ling 3.0 Flash Findon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Ling 3.0 Flash Fin costs less

For input processing, Claude 3.5 Haiku ($0.80/1M tokens) is 13.3x more expensive than Ling 3.0 Flash Fin ($0.06/1M tokens).

For output processing, Claude 3.5 Haiku ($4.00/1M tokens) is 22.2x more expensive than Ling 3.0 Flash Fin ($0.18/1M tokens).

In conclusion, Claude 3.5 Haiku is more expensive than Ling 3.0 Flash Fin.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Sep 08 2026 • llm-stats.com
Anthropic
Claude 3.5 Haiku
Input tokens$0.80
Output tokens$4.00
Best providerAWS Bedrock
InclusionAI
Ling 3.0 Flash Fin
Input tokens$0.06
Output tokens$0.18
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

Ling 3.0 Flash Fin accepts 262,144 input tokens compared to Claude 3.5 Haiku's 200,000 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while Claude 3.5 Haiku is limited to 200,000 tokens.

Anthropic
Claude 3.5 Haiku
Input200,000 tokens
Output200,000 tokens
InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Tue Sep 08 2026 • llm-stats.com

Release Timeline

When each model was launched

Claude 3.5 Haiku was released on 2024-10-22, while Ling 3.0 Flash Fin was released on 2026-09-03.

Ling 3.0 Flash Fin is 23 months newer than Claude 3.5 Haiku.

Claude 3.5 Haiku

Oct 22, 2024

1.9 years ago

Ling 3.0 Flash Fin

Sep 3, 2026

5 days ago

1.9yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Claude 3.5 Haiku is available from Bedrock, Google, Anthropic. Ling 3.0 Flash Fin is available from DeepInfra.

Claude 3.5 Haiku

bedrock logo
AWS Bedrock
Input Price:Input: $0.80/1MOutput Price:Output: $4.00/1M
google logo
Google
Input Price:Input: $0.80/1MOutput Price:Output: $4.00/1M
anthropic logo
Anthropic
Input Price:Input: $1.00/1MOutput Price:Output: $5.00/1M

Ling 3.0 Flash Fin

deepinfra logo
Deepinfra
Input Price:Input: $0.06/1MOutput Price:Output: $0.18/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Claude 3.5 Haiku and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.

Claude 3.5 Haiku
✓ Preferred
Ling 3.0 Flash Fin
Open in Playground

FAQ

Common questions about Claude 3.5 Haiku vs Ling 3.0 Flash Fin.

Which is better, Claude 3.5 Haiku or Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 5.3. Claude 3.5 Haiku is made by Anthropic and Ling 3.0 Flash Fin is made by InclusionAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Claude 3.5 Haiku compare to Ling 3.0 Flash Fin in benchmarks?

Claude 3.5 Haiku scores HumanEval: 88.1%, MGSM: 85.6%, DROP: 83.1%, MATH: 69.4%, MMLU-Pro: 65.0%. Ling 3.0 Flash Fin scores SpreadSheetBench-v1: 86.5%, Finance Agent v1.1: 69.2%, Finance Agent v2: 59.8%, Tau3 Banking: 41.0%, APEX-Agents: 29.2%.

Is Claude 3.5 Haiku cheaper than Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin is 13.3x cheaper for input tokens. Claude 3.5 Haiku costs $0.80/M input and $4.00/M output via bedrock. Ling 3.0 Flash Fin costs $0.06/M input and $0.18/M output via deepinfra.

What are the context window sizes for Claude 3.5 Haiku and Ling 3.0 Flash Fin?

Claude 3.5 Haiku supports 200K tokens and Ling 3.0 Flash Fin supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Claude 3.5 Haiku and Ling 3.0 Flash Fin?

Key differences include LLM Stats Score (5.3 vs 43.3), context window (200K vs 262K), input pricing ($0.80 vs $0.06/M), licensing (Proprietary vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes Claude 3.5 Haiku and Ling 3.0 Flash Fin?

Claude 3.5 Haiku is developed by Anthropic and Ling 3.0 Flash Fin is developed by InclusionAI.