Model profile
Tencent
New in 2026

Tencent: Hy3

Tencent: Hy3 is a budget-priced text-first model from Tencent with balanced runtime profile, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsBalanced latencyLarge contextBudget pricing
Intelligence
42.2

Benchmark blend

Coding
58.8

Dev workflow signal

Context
262K Tokens

Large

Input Price
$0.14

Budget tier

Decision snapshot
62

Tencent: Hy3 currently reads as a budget text-first option with large context and a balanced runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Agent workflows
Latency tier
Balanced
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

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
42.2
42

General reasoning and benchmark headroom.

Situational
Speed
76 tok/s
57

TTFT 1.85s

Situational
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.14
86

$0.55 output / 1M

Efficient

Editorial Profile

Tencent: Hy3 in one narrative

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

Selective fitCoding score 59Math score N/A

Hy3 is a 295B-parameter Mixture-of-Experts model from Tencent (21B active, 192 experts with top-8 routing) built for reasoning, agentic workflows, and real-world production use. It supports a configurable reasoning effort:...

Identity

Tencent text-first profile

Positioning

Long-context research / Agent workflows with large context and balanced 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 is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • 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.

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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.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
42.2
GPQA
89.7%
HLE
33.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
58.8
SciCode
47.6%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

LCR
74.7%

Extra benchmark cuts are not available for this category 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
262K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
128000
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biasmax_completion_tokensmax_tokensmin_ppresence_penaltyreasoningreasoning_effortrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.14
Output
per 1M output tokens
$0.55
Blended
AA 3:1 mix
$0.24

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

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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