Hy3 vs Ling-3.0-flash

    Comparison

    As of August 20, 2026, Ling-3.0-flash is 64% cheaper per million tokens than Hy3. Hy3 is $0.13 / $0.53 per million input/output tokens versus Ling-3.0-flash at $0.06 / $0.18. Sources: https://www.tencentcloud.com/document/product/1300/78937 and https://developer.ant-ling.com/en/docs/models/ling/

    Sources: Tencent pricing (https://www.tencentcloud.com/document/product/1300/78937); inclusionAI pricing (https://developer.ant-ling.com/en/docs/models/ling/)

    Hy3

    Tencent

    $0.66blended / 1M

    Input

    $0.13

    Output

    $0.53

    262.1K ctx|Open Source
    64%
    cheaper
    Better value

    Ling-3.0-flash

    inclusionAI

    $0.24blended / 1M

    Input

    $0.06

    Output

    $0.18

    262.1K ctx|Proprietary
    Save $0.42 per million tokens by choosing Ling-3.0-flash over Hy3.

    Hy3 vs Ling-3.0-flash comparison

    Token pricing, specs, and benchmarks side by side

    Metric
    Hy3
    Ling-3.0-flash
    Provider
    Provider
    Tencent
    inclusionAI
    License
    Open Source
    Proprietary
    Release Date
    2026-07-06
    2026-07-23
    Pricing (per 1M tokens)
    Input Price+120%
    $0.13
    $0.06
    Output Price+193%
    $0.53
    $0.18
    Blended (1M + 1M)
    $0.66
    $0.24
    Model Details
    Context Window
    262.1K
    262.1K
    Max Output Tokens
    N/A
    32.8K
    Knowledge Cutoff
    N/A
    N/A
    Throughput
    N/A
    1000 tok/s

    Verdict

    Hy3 vs Ling-3.0-flash: the bottom line

    Both models are closely matched on benchmarks. Ling-3.0-flash has the pricing advantage, making it the better value unless Hy3's specific capabilities are critical to your workflow.

    Hy3 vs Ling-3.0-flash FAQ

    Which is cheaper, Hy3 or Ling-3.0-flash?

    As of August 20, 2026, Ling-3.0-flash is 64% cheaper on blended LLM API pricing. Ling-3.0-flash costs $0.06 per million input tokens and $0.18 per million output tokens ($0.24 blended 1M-in + 1M-out). Hy3 costs $0.13 / $0.53 per million tokens ($0.66 blended). Sources: https://www.tencentcloud.com/document/product/1300/78937 and https://developer.ant-ling.com/en/docs/models/ling/. The cheapest LLM for your app still depends on how many output tokens you generate.

    How much does Hy3 vs Ling-3.0-flash cost per million tokens?

    Hy3 Tencent API pricing is $0.13 input and $0.53 output per million tokens via Tencent. Ling-3.0-flash inclusionAI API pricing is $0.06 input and $0.18 output per million tokens via inclusionAI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.

    Which is better for coding, Hy3 or Ling-3.0-flash?

    Hy3 has a published SWE-bench Verified score of 78%. Ling-3.0-flash does not have that eval on this page yet.

    What is the context window for Hy3 vs Ling-3.0-flash?

    Hy3 supports a 262K token context window, while Ling-3.0-flash supports 262K tokens. Both models have the same context window size. 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, Hy3 or Ling-3.0-flash?

    Shared benchmark scores for Hy3 and Ling-3.0-flash 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 Hy3 or Ling-3.0-flash better for production use?

    Both Hy3 and Ling-3.0-flash are production API models. For cost-sensitive production traffic, Ling-3.0-flash 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 Hy3 and Ling-3.0-flash in my app?

    Yes. Call Hy3 through Tencent and Ling-3.0-flash through inclusionAI 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 Hy3 vs Ling-3.0-flash pricing data?

    List rates are the latest published Tencent API pricing and inclusionAI API pricing, quoted per million tokens. Official rate sheets: https://www.tencentcloud.com/document/product/1300/78937 and https://developer.ant-ling.com/en/docs/models/ling/. 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 Hy3 and Ling-3.0-flash?

    Hy3 supports up to an unspecified number of output tokens per request, while Ling-3.0-flash supports up to 33K 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, Hy3 or Ling-3.0-flash?

    Ling-3.0-flash has a measured throughput of approximately 1000 tokens/second. Throughput data for Hy3 is not yet available. 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 Hy3 vs Ling-3.0-flash comparison?

    Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Hy3 or Ling-3.0-flash from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.

    Hy3 vs Ling-3.0-flash pricing

    This comparison covers API pricing, context window, throughput, and benchmark scores. Hy3 costs $0.13/M input and $0.53/M output. Ling-3.0-flash costs $0.06/M input and $0.18/M output.

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

    The index

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