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text model comparison

ERNIE 5.5 Flash Preview vs Hy4

ERNIE 5.5 Flash Preview and Hy4 are both current text models, from Baidu and Tencent. ERNIE 5.5 Flash Preview is 66% cheaper per 1M input tokens than Hy4, $0.28 vs $0.834. In Baidu's ERNIE 5.5 Flash Preview launch table, ERNIE 5.5 Flash Preview scores 46.5% on Terminal-Bench 4.0 and Hy4 42.4%.

Lower price per 1M input tokens
ERNIE 5.5 Flash Preview$0.28 against $0.834
Newer release
ERNIE 5.5 Flash PreviewNov 11, 2026 against Nov 4, 2026

Data verified November 11, 2026 · Dataset 2026-11-11

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Side by side

ERNIE 5.5 Flash Preview and Hy4, line by line

Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.

FactERNIE 5.5 Flash PreviewHy4
Price
Price per 1M input tokens$0.28 (better)$0.834
Price per 1M output tokens$1.13 (better)$2.501
Specs
ProviderBaiduTencent
Release dateNovember 11, 2026November 4, 2026
AccessAPI onlyAPI only
Context window1,000,000 tokensNot reported
Max output1,000,000 tokensNot reported
Inputtexttext, image
Outputtexttext

Provider results

What the labs report

Results tables a lab published with one of its models, where it lists both ERNIE 5.5 Flash Preview and Hy4. These are the lab's own figures, not independent measurements, and each table is read on its own: labs run benchmarks with different harnesses and budgets.

Baidu, ERNIE 5.5 Flash Preview launch table, November 11, 2026Baidu · 2026-11-11

BenchmarkERNIE 5.5 Flash PreviewHy4
Terminal-Bench 4.046.5% (better)42.4%
DeepSWE v1.171.2%74.9% (better)
ProgramBenchalmost solved29.5% (better)28.5%
NL2Repo64.8%65.8% (better)
Terminal-Bench-Science 0.121.0% (better)17.1%
CookieBench88.2%88.8% (better)
BlenderWorldtext-only subset79.0% (better)76.8%
AutomationBench v1.0.6open-sourced subset61.9% (better)55.2%
APEX-Agents v1.164.2%75.0% (better)
DRACO84.9%90.7% (better)
SpreadsheetBench 236.4% (better)34.5%
OfficeQA Pro v267.9% (better)63.8%
Agents' Last Examcommand-line tasks29.0%32.4% (better)
SkillsBench v1.1text-only subset73.5%77.4% (better)
Toolathlon-Verified75.0% (better)73.1%
MCP Atlaspublic set84.6% (better)83.0%
BrowseComp87.4%91.8% (better)
Humanity's Last Examwithout tools, text-only46.4%46.7% (better)

Baidu's appendix to the ERNIE 5.5 Flash Preview announcement, every model at its highest reasoning setting. Where Baidu lists a lab's reported score beside its own run, the score Baidu measured is shown. DeepSeek-V4.1-Flash is the 1017 build. Left out here: AA-Omniscience-Public and AA-LCR (Artificial Analysis benchmarks), GPT-6.1 Sol on Terminal-Bench 4.0 (cited from Artificial Analysis), and the full-set Humanity's Last Exam scores in a row Baidu otherwise ran on the text-only subset.

Charts

Against every current text model

Where ERNIE 5.5 Flash Preview and Hy4 land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

No independent benchmark has scored either model yet, so the tiles rank their prices among the current models of their task.

ERNIE 5.5 Flash Preview and Hy4 among the current text models, one row per benchmark, price and context window.

Price and context

Price per 1M input tokens

ERNIE 5.5 Flash Preview:#12of 57

$0.28 · cheaper than 80%

Hy4:#24of 57

$0.834 · cheaper than 59%

Price per 1M output tokens

ERNIE 5.5 Flash Preview:#12of 57

$1.13 · cheaper than 80%

Hy4:#23of 57

$2.501 · cheaper than 61%

Context window

ERNIE 5.5 Flash Preview:#29of 67

1M tokens · longer than 24%

Hy4: Not listed

Cost

What each costs

Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.

The same work, priced

For 10M input and 2M output tokens, Hy4 costs 2.6 times as much as ERNIE 5.5 Flash Preview, at each provider's listed rates.

What ERNIE 5.5 Flash Preview and Hy4 cost for the same work, at the rates each provider lists.
  • 10M input and 2M output tokens

    Hy4 costs 2.6× as much

    ERNIE 5.5 Flash Preview
    $5.06
    Hy4
    $13.34
  • The same, with 9M of the input tokens cached

    Hy4 costs 2.2× as much

    ERNIE 5.5 Flash Preview
    $2.79
    Hy4
    $6.21
Workload or rateERNIE 5.5 Flash PreviewHy4
Example workloads
10M input and 2M output tokens$5.06 (better)$13.34
The same, with 9M of the input tokens cached$2.79 (better)$6.21
Other listed rates
1M input tokens, cached$0.028 (better)$0.042

Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.

Launch price $100, regular price $150

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Verdict

When to choose each

ERNIE 5.5 Flash Preview

Where it leads

  • Lower input and output prices, $0.28 and $1.13 vs $0.834 and $2.501 per 1M tokens
  • Cheaper cached input, $0.028 vs $0.042 per 1M tokens

Best for

  • Low-cost agents for spreadsheets, office documents and tool calls over MCP, where text input is enough
  • Long-horizon coding and game or 3D scene building inside an agent harness, at a Flash-tier price

Watch out for

  • A preview, served only through Baidu Qianfan: Baidu says the weights will be open-sourced under Apache 2.0 soon, but they are not out yet
  • Text only: it cannot read images, and in Baidu tests that needed vision a tool running Claude Opus 5.5 described the screen for it
  • In Baidu's own table it trails Hy4 on APEX-Agents 1.1 (64.2% against 75.0%) and DeepSeek-V4.1-Flash on DeepSWE v1.1 (71.2% against 77.0%), and stays far behind GPT-6.1 Sol and Claude Opus 5.5 on Terminal-Bench-Science 0.1 (21.0% against 57.0% and 63.3%)
  • Epoch AI and Arena have not scored it yet

Hy4

Where it leads

  • Accepts image input

Best for

  • Agents that work across terminals, desktop apps and browsers on a tight budget
  • Finance and office work: research reports, spreadsheets and slides

Watch out for

  • API only for now: Tencent says it will publish the weights and a system card later, and only Hy4 preview (August 2026, Apache 2.0) is open today
  • In Tencent's own table it scores 44.4% on Terminal-Bench 4.0 against 66.8% for Kimi K3.1 Pro, and Tencent says a clear gap remains to top proprietary models on research-level math, long-horizon coding and hard visual questions
  • Tencent publishes no context window or output limit for Hy4 (the preview card lists 1M tokens), and no independent benchmark score is recorded here yet

From benchmark to production

Building on ERNIE 5.5 Flash Preview or Hy4?

Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.

AI Model Cost AuditLaunch price $100, regular price $150What the audit includes

FAQ

ERNIE 5.5 Flash Preview vs Hy4 FAQ

Which is cheaper, ERNIE 5.5 Flash Preview or Hy4?

ERNIE 5.5 Flash Preview is 66% cheaper per 1M input tokens than Hy4, $0.28 vs $0.834. For 10M input and 2M output tokens, ERNIE 5.5 Flash Preview costs $5.06 and Hy4 $13.34 at standard rates.

Which is cheaper with prompt caching, ERNIE 5.5 Flash Preview or Hy4?

ERNIE 5.5 Flash Preview charges $0.028 per 1M cached input tokens, 90% below its standard input rate, and Hy4 $0.042, 95% below. With 9M of 10M input tokens cached and 2M output tokens, ERNIE 5.5 Flash Preview costs $2.79 and Hy4 $6.21.

How do ERNIE 5.5 Flash Preview and Hy4 compare in Baidu's own results?

In the ERNIE 5.5 Flash Preview launch table (November 11, 2026), ERNIE 5.5 Flash Preview leads on Terminal-Bench 4.0, 46.5% to 42.4%; Hy4 leads on DeepSWE v1.1, 74.9% to 71.2%; ERNIE 5.5 Flash Preview leads on ProgramBench (almost solved), 29.5% to 28.5%. These are Baidu's figures, not independent measurements.

How do ERNIE 5.5 Flash Preview and Hy4 differ in features?

ERNIE 5.5 Flash Preview takes text input; Hy4 takes text and image.

Production evaluation

Choosing between ERNIE 5.5 Flash Preview and Hy4?

Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • A head-to-head evaluation on your real workload
  • Routing between models by cost and difficulty