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Model Comparison

Llama 3.1 Euryale 70B v2.2 vs Hy-MT2-1.8BWhich Is Better in 2026?

Sao10K•Tencent: Hy-MT2-1.8B logoTencent

Llama 3.1 Euryale 70B v2.2 vs Hy-MT2-1.8B: which should you choose in 2026?

Llama 3.1 Euryale 70B v2.2 (by Sao10K) and Hy-MT2-1.8B (by Tencent) 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.

Hy-MT2-1.8B is about 11.0× cheaper on a blended 3:1 input/output basis ($0.07725 vs $0.85 per 1M tokens).

Llama 3.1 Euryale 70B v2.2 has the larger context window (131,072 tokens vs 8,192 tokens).

Choose Llama 3.1 Euryale 70B v2.2 if…

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

Choose Hy-MT2-1.8B 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.

Llama 3.1 Euryale 70B v2.2 and Hy-MT2-1.8B benchmark results
BenchmarkLlama 3.1 Euryale 70B v2.2Hy-MT2-1.8B
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

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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

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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

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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

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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

    Llama 3.1 Euryale 70B v2.2 and Hy-MT2-1.8B model facts
    FeatureLlama 3.1 Euryale 70B v2.2Hy-MT2-1.8B
    Context & model facts
    DeveloperSao10KTencent
    API providerSao10KTencent
    Input context131,072 tokens8,192 tokens
    Maximum output16,384 tokens4,096 tokens
    Released——
    Added to WritingmateAug 28, 2024Aug 20, 2026
    LicenseNot availableNot available
    Knowledge cutoff2023-12-31—
    Capabilities
    InputsTextText
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningNoNo
    VisionNoNo
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.85$0.044
    Output (per 1M tokens)$0.85$0.177
    Blended 3:1 input/output$0.85$0.07725
    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 Aug 20, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Llama 3.1 Euryale 70B v2.2 from Sao10K with Hy-MT2-1.8B from Tencent? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Llama 3.1 Euryale 70B v2.2 and Hy-MT2-1.8B
    PlanPriceLlama 3.1 Euryale 70B v2.2Hy-MT2-1.8BAI 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

    Llama 3.1 Euryale 70B v2.2 vs Hy-MT2-1.8B FAQ

    Which is better, Llama 3.1 Euryale 70B v2.2 or Hy-MT2-1.8B?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Hy-MT2-1.8B is about 11.0× cheaper on a blended 3:1 input/output basis ($0.07725 vs $0.85 per 1M tokens). Llama 3.1 Euryale 70B v2.2 has the larger context window (131,072 tokens vs 8,192 tokens).

    Which model is cheaper to use through an API?

    Hy-MT2-1.8B is about 11.0× cheaper on a blended 3:1 input/output basis ($0.07725 vs $0.85 per 1M tokens).

    Which model supports more context?

    Llama 3.1 Euryale 70B v2.2 has the larger context window (131,072 tokens vs 8,192 tokens).

    Can I switch between Llama 3.1 Euryale 70B v2.2 and Hy-MT2-1.8B?

    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.