Tencent · model passport

Tencent: Hy3 preview

hy3-preview

Open weights
tencent-hunyuan-community licence
298.8B params
262,144 context
Released 2026-04-13(HF repo created)

Best percentile: 71.9IFBench (Artificial Analysis)
Cohort standing: 49.4median 63.0

Thin coverage — read with caution

This model appears on only 2 of 27 benchmark axes considered. The overall standing has been shrunk toward the neutral 50 to reflect the small sample, and no weaknesses have been claimed — a low percentile on 2 axes is a coverage artifact, not a measured weakness.

What the data says

Benchmarked on 2 of 27 tracked axes. Every figure below is the model's own published result on that board.

On only 2 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.

Uneven: 46.7 percentile points between its best and worst axis — 71.9 pct on IFBench (Artificial Analysis) versus 25.2 pct on WebDev Arena.

IFBench (Artificial Analysis)71.9 pct63.1%Verify →
WebDev Arena25.2 pct1,357Verify →
OverallNot benchmarked
Vision ArenaNot benchmarked
Factuality ArenaNot benchmarked
Search ArenaNot benchmarked
Document ArenaNot benchmarked
Agent ArenaNot benchmarked
GPQA DiamondNot benchmarked
AIME (math)Not benchmarked
FrontierMathNot benchmarked
SimpleQA VerifiedNot benchmarked
ARC-AGI-2Not benchmarked
Humanity's Last ExamNot benchmarked
SimpleBenchNot benchmarked
LiveBenchNot benchmarked
BFCL v4 (function calling)Not benchmarked
SWE-bench Verified (live board)Not benchmarked
Terminal-Bench 4.0Not benchmarked
MMLU-Pro (Artificial Analysis)Not benchmarked
LiveCodeBench (Artificial Analysis)Not benchmarked
HHEM Hallucination (Vectara)Not benchmarked
SWE-bench VerifiedNot benchmarked
Terminal-BenchNot benchmarked
CodingNot benchmarked
Instruction followingNot benchmarked
Hard promptsNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorytencent/Hy3-preview

Parameters298.8B

ArchitectureHYV3ForCausalLM

Licencetencent-hunyuan-community

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tencent/Hy3-preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Use it hosted

API model IDtencent/hy3-preview

Context length262,144 tokens

Max output235,929 tokens

Input price$0.18 per 1M tokens

Cached input price$0.06 per 1M tokens

Output price$0.60 per 1M tokens

API parameters this endpoint supports

Include reasoningMax tokensReasoningReasoning effortSeedTemperatureTool choiceTool callingTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"tencent/hy3-preview","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

Hy3 preview stands at 49.4 (raw mean 48.5) — measured on only 2 of 27 axes, so the standing is shrunk toward the neutral 50.
standingtemplateVerify →
Hy3 preview stands at 49.4 versus a cohort median of 63 across 145 models released within 180 days — below the peer median.
cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →