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tencent

Tencent: Hunyuan A13B Instruct

Tencent: Hunyuan A13B Instruct is a budget text-first model from tencent with a heavy runtime profile, extended context posture, and the clearest fit around long-context research / reasoning.

Best for: Long-context research / ReasoningHeavy latencyExtended contextBudget pricing
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.14

Budget tier

Decision snapshot
56

Tencent: Hunyuan A13B Instruct currently reads as a budget text-first option with extended context and a heavy runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Reasoning
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouter
Compare this modelSee leaderboard

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
N/A
44

General reasoning and benchmark headroom.

Situational
Speed
N/A
46

Latency data is partial.

Situational
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.14
86

$0.57 output / 1M

Efficient

Editorial Profile

Tencent: Hunyuan A13B Instruct in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 40Math score 36

Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark performance across mathematics, science, coding, and multi-turn reasoning tasks, while maintaining high inference efficiency via Grouped Query Attention (GQA) and quantization support (FP8, GPTQ, etc.).

Identity

tencent text-first profile

Positioning

Long-context research / Reasoning with extended context and heavy runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths

  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs

  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit

  • Long-context summarization, repo analysis, and policy or document review.

Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

No benchmark data is available for this model yet.

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
131K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Other
Max Completion
131072
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningreasoningresponse_formatstructured_outputstemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.14
Output
per 1M output tokens
$0.57
Blended
AA 3:1 mix
N/A

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.