Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct

    Comparison

    As of August 20, 2026, Qwen2.5-Coder 32B Instruct is 25% cheaper per million tokens than Ling-3.0-flash. Ling-3.0-flash is $0.06 / $0.18 per million input/output tokens versus Qwen2.5-Coder 32B Instruct at $0.09 / $0.09. Sources: https://developer.ant-ling.com/en/docs/models/ling/ and https://bailian.console.aliyun.com/

    Sources: inclusionAI pricing (https://developer.ant-ling.com/en/docs/models/ling/); Qwen pricing (https://bailian.console.aliyun.com/)

    Ling-3.0-flash

    inclusionAI

    $0.24blended / 1M

    Input

    $0.06

    Output

    $0.18

    262.1K ctx|Proprietary
    25%
    cheaper
    Better value

    Qwen2.5-Coder 32B Instruct

    Qwen

    $0.18blended / 1M

    Input

    $0.09

    Output

    $0.09

    128K ctx|Open Source
    Save $0.06 per million tokens by choosing Qwen2.5-Coder 32B Instruct over Ling-3.0-flash.

    Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct comparison

    Token pricing, specs, and benchmarks side by side

    Metric
    Ling-3.0-flash
    Qwen2.5-Coder 32B Instruct
    Provider
    Provider
    inclusionAI
    Qwen
    License
    Proprietary
    Open Source
    Release Date
    2026-07-23
    2024-09-19
    Pricing (per 1M tokens)
    Input Price-33%
    $0.06
    $0.09
    Output Price+100%
    $0.18
    $0.09
    Blended (1M + 1M)
    $0.24
    $0.18
    Model Details
    Context Window
    262.1K
    128K
    Max Output Tokens
    32.8K
    128K
    Knowledge Cutoff
    N/A
    N/A
    Throughput
    1000 tok/s
    42 tok/s
    Benchmarks
    75.1%
    83%
    92.7%
    57.2%
    70.5%
    91.1%
    46.2%
    90.2%
    50.4%
    77.5%
    43.1%
    54.2%
    80.8%

    Verdict

    Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct: the bottom line

    Both models are closely matched on benchmarks. Qwen2.5-Coder 32B Instruct has the pricing advantage, making it the better value unless Ling-3.0-flash's specific capabilities are critical to your workflow.

    Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct FAQ

    Which is cheaper, Ling-3.0-flash or Qwen2.5-Coder 32B Instruct?

    As of August 20, 2026, Qwen2.5-Coder 32B Instruct is 25% cheaper on blended LLM API pricing. Qwen2.5-Coder 32B Instruct costs $0.09 per million input tokens and $0.09 per million output tokens ($0.18 blended 1M-in + 1M-out). Ling-3.0-flash costs $0.06 / $0.18 per million tokens ($0.24 blended). Sources: https://developer.ant-ling.com/en/docs/models/ling/ and https://bailian.console.aliyun.com/. The cheapest LLM for your app still depends on how many output tokens you generate.

    How much does Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct cost per million tokens?

    Ling-3.0-flash inclusionAI API pricing is $0.06 input and $0.18 output per million tokens via inclusionAI. Qwen2.5-Coder 32B Instruct Qwen API pricing is $0.09 input and $0.09 output per million tokens via Qwen. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.

    Which is better for coding, Ling-3.0-flash or Qwen2.5-Coder 32B Instruct?

    Qwen2.5-Coder 32B Instruct has a published SWE-bench Verified score of 9%. Ling-3.0-flash does not have that eval on this page yet.

    What is the context window for Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct?

    Ling-3.0-flash supports a 262K token context window, while Qwen2.5-Coder 32B Instruct supports 128K tokens. Ling-3.0-flash offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.

    Which model performs better on benchmarks, Ling-3.0-flash or Qwen2.5-Coder 32B Instruct?

    Shared benchmark scores for Ling-3.0-flash and Qwen2.5-Coder 32B Instruct are still limited. Check the comparison table for the evals each model has published. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.

    Is Ling-3.0-flash or Qwen2.5-Coder 32B Instruct better for production use?

    Both Ling-3.0-flash and Qwen2.5-Coder 32B Instruct are production API models. For cost-sensitive production traffic, Qwen2.5-Coder 32B Instruct has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.

    Can I switch between Ling-3.0-flash and Qwen2.5-Coder 32B Instruct in my app?

    Yes. Call Ling-3.0-flash through inclusionAI and Qwen2.5-Coder 32B Instruct through Qwen with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.

    How accurate is this Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct pricing data?

    List rates are the latest published inclusionAI API pricing and Qwen API pricing, quoted per million tokens. Official rate sheets: https://developer.ant-ling.com/en/docs/models/ling/ and https://bailian.console.aliyun.com/. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.

    What is the output token limit for Ling-3.0-flash and Qwen2.5-Coder 32B Instruct?

    Ling-3.0-flash supports up to 33K output tokens per request, while Qwen2.5-Coder 32B Instruct supports up to 128K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.

    Which model has better throughput, Ling-3.0-flash or Qwen2.5-Coder 32B Instruct?

    Ling-3.0-flash runs at approximately 1000 tokens/second while Qwen2.5-Coder 32B Instruct runs at 42 tokens/second. Ling-3.0-flash is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.

    How do I switch the models in this Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct comparison?

    Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Ling-3.0-flash or Qwen2.5-Coder 32B Instruct from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.

    Ling-3.0-flash vs Qwen2.5-Coder 32B Instruct pricing

    This comparison covers API pricing, context window, throughput, and benchmark scores. Ling-3.0-flash costs $0.06/M input and $0.18/M output. Qwen2.5-Coder 32B Instruct costs $0.09/M input and $0.09/M output.

    Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.

    The index

    All Large Language Models

    280 models across 33 providers. Search or jump to a lab — every model page stays linked here.

    Baidu

    1 models

    Inception

    1 models

    inclusionAI

    0 models

    LG AI Research

    1 models

    Nous Research

    1 models

    Sakana AI

    1 models

    StepFun

    1 models

    Tencent

    1 models

    Thinking Machines

    1 models

    Unisound

    1 models

    Upstage

    1 models

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