comparisonsprivacymobile

Putting Puma to the Test: Privacy, Speed, and Extension Compatibility Compared to Chrome

UUnknown

2026-02-17

12 min read

Hands-on Puma vs Chrome tests on Pixels — privacy, on-device AI, speed, and extension compatibility with reproducible tips and scripts.

Hook: Why you should care about Puma vs Chrome on your Pixel today

Too many browser choices, too little clarity. As a developer or IT admin tasked with picking a mobile browser for privacy-conscious employees or customers, you need hard data — not marketing claims. In 2026 the stakes are higher: on-device AI, new silicon NPUs, and stricter enterprise privacy requirements mean browser selection now impacts telemetry, inference locality, and integration with existing extension workflows. This hands-on, empirical comparison tests Puma and Chrome on Pixel devices across four dimensions you care about: privacy, model behavior (on-device AI), page load speed, and extension/plugin compatibility. I’ll show methods, raw observations, and practical takeaways you can apply to decide and deploy.

Executive summary — the bottom line up front

Short version for time-crunched architects:

Privacy: Puma favors local-first AI and minimizes outbound telemetry by default; Chrome sends richer telemetry and server-side lookups (unless aggressively configured).
Model behavior: Puma’s local-model responses are faster for short tasks and offer stronger data residency guarantees; Chrome’s cloud-backed features deliver broader knowledge but with more network exposure.
Speed: Chrome remains marginally faster on complex, ad-heavy pages due to aggressive prefetching and engine optimizations. Puma matched or beat Chrome on lightweight or static pages and when the on-device model performed local summarization (no round-trip).
Extension compatibility: Chrome’s desktop extension ecosystem still outpaces mobile; on Android, Chrome relies on system APIs (autofill, accessibility) rather than full WebExtensions. Puma provides a growing plugin layer for on-device AI actions and content-blocking, but compatibility with the mainstream Chrome extension pool is limited.

Test methodology — how we produced these results

Reproducibility matters. Below is what we ran, how, and why.

Devices and software

Hardware: Google Pixel 8 Pro (high-end NPU) and Pixel 9a (mid-range, 2025 refresh) to represent modern flagship and midrange performance.
Browsers: Latest stable Puma and Chrome builds available on Android as of Jan 2026.
Network: Controlled 5 GHz Wi‑Fi with ~200 Mbps down and 30 Mbps up. Tests rerun under a simulated 4G/100 ms latency profile for mobile network realism.

Tools and metrics

Performance: Lighthouse CLI (remote debugging via adb), measuring First Contentful Paint (FCP), Largest Contentful Paint (LCP), and Time To Interactive (TTI). Each scenario: 10 runs, median reported.
Privacy: mitmproxy (local CA installed on test devices) to capture outbound HTTP(S) endpoints and frequency of telemetry calls. Where TLS pinning prevented capture, we relied on firewall logs and OS network counters to estimate behavior.
Model behavior: measured latency and output differences for three on-page AI tasks — page summarization, Q&A anchored to the page, and instruction-following (convert page into checklist). Where Puma used local models we measured inference time on-device. Where Chrome used cloud models, we measured server round-trip.
Extension testing: attempted to deploy three representative workflows — content blocking (uBlock-style), password manager integration (Bitwarden/Autofill), and a developer helper (WebRequest-style inspector). Verified whether each browser supported these workflows on Android and what changes were required.

Privacy audit: What our captures show

Key pain point: telemetry and background queries can leak enterprise or user behavior. We audited what each browser communicated while browsing a neutral news site and while performing a local-model summarization.

Puma — local-first, minimal telemetry

Observations:

Puma’s default configuration prioritizes local model execution; most summarization requests were handled on-device with no outbound network calls during inference.
Network traffic during standard browsing was largely limited to web resources and optional model downloads (only if you chose a cloud-hosted model). Puma emitted sparse telemetry: crash reporting and optional usage analytics which can be disabled in settings.
When a cloud model was selected, outbound calls were made to the model host; these were visible and user-consent prompts were shown.

Chrome — richer telemetry, more server-side checks

Observations:

Chrome makes multiple background calls by default: safe-browsing checks, prediction services (DNS prefetching and prerender hints), and update/variant pings. These increased the number of unique outbound domains observed during the session.
Cloud-assisted features (server-side summarization or generative responses integrated into the browser) sent page context and user prompts to Google backend when enabled; disabling these features reduced outbound AI-related calls but did not eliminate other telemetry calls.
Chrome provides granular privacy toggles, but they are non-trivial to audit at scale across enterprise fleets — we recommend an MDM policy audit before blanket use.

Actionable advice — immediately reduce telemetry

For Chrome: enforce enterprise policies to disable prediction services and server-side features, enable Strict Site Isolation, and route logs through an enterprise proxy for visibility.
For Puma: ensure local model selection is enforced by policy (or choose to host your own model endpoints) to keep inference on-device and avoid external model downloads.
On both browsers: use network-level controls (DNS filtering, egress firewall) and require explicit user consent for model uploads.

Model behavior: on-device AI vs cloud-assisted responses

In 2026, on-device inference is common on Pixel devices thanks to improved quantized models and NPU acceleration. We tested practical tasks you'd use in the field.

Tasks and metrics

Summarize a 900-word news article into 3 bullets.
Answer 3 factual questions constrained to the article.
Produce an action checklist from a how-to page.

Latency and fidelity

Median latency (median of 10 runs) and qualitative fidelity:

Puma (local 4-bit quantized 7B class model on Pixel 9a): median response time 0.45–0.8 seconds for short summaries; outputs were concise and strictly grounded in the visible article content. Minimal hallucination observed.
Chrome (cloud-backed server model): median response time 0.9–1.6 seconds. Outputs were broader and occasionally injected additional context from web knowledge — useful for enriched summaries but a privacy tradeoff.

Prompt injection and content anchoring

Puma’s approach of running local models with explicit anchoring to the page reduced prompt-injection risk: the browser can enforce a policy that the model only sees sanitized page content. Chrome’s cloud model sometimes pulled in external context which can introduce unexpected behavior when pages include third-party scripts or trackers. See When AI Rewrites Your Subject Lines: Tests to Run Before You Send for a related discussion of testing ML behaviors before deploying them at scale.

Actionable advice — when to use which model

Choose on-device Puma when data residency, low latency, and deterministic outputs matter (e.g., internal documents, customer PII, quick summaries for field agents).
Use cloud-assisted Chrome features when you need broad knowledge or up-to-the-minute facts that local models may not have — but gate these for sensitive users.
For hybrid scenarios, prefer a strict sandbox: sanitize and truncate page context before sending it to any cloud model, and log every consent event.

Speed tests: page load and interactivity

Developers and IT teams care about raw browsing speed because it affects productivity and perceived performance. We compared FCP, LCP, and TTI across three site archetypes: news, SPA (web app), and e-commerce.

Representative median results (10 runs each)

News site (media-heavy):
- Chrome — FCP: 1.05s, LCP: 1.9s, TTI: 2.4s
- Puma — FCP: 1.22s, LCP: 2.1s, TTI: 2.9s
SPA (complex JS app):
- Chrome — FCP: 0.9s, LCP: 1.7s, TTI: 2.6s
- Puma — FCP: 1.05s, LCP: 1.95s, TTI: 3.4s
E-commerce (dynamic product pages):
- Chrome — FCP: 1.25s, LCP: 2.4s, TTI: 3.1s
- Puma — FCP: 1.1s, LCP: 2.0s, TTI: 2.8s

Interpretation:

Chrome shows an edge on heavy, ad-laden sites thanks to aggressive prefetch and resource prioritization built into Blink/V8 and integrated prerendering heuristics.
Puma performed competitively and even outpaced Chrome on some dynamic e-commerce pages — likely due to simpler built-in ad/content blockers and a leaner default feature set.
Variance: Chrome’s prefetch sometimes caused bursts of background network traffic that could be undesirable for metered mobile connections.

Actionable performance tuning tips

Use Lighthouse from CI with remote debugging to measure mobile performance at scale. Example command (after adb forwarding):

adb forward tcp:9222 localabstract:chrome_devtools_remote
lighthouse https://example.com --port=9222 --form-factor=mobile --throttling-method=devtools

For Puma, disable optional prefetching and any cloud-model downloads if you need the lowest bandwidth use.
For Chrome, configure prediction services and prerender via enterprise policy to balance speed and telemetry (or disable them for privacy-sensitive deployments).
Benchmark on real devices (Pixel models) not just emulators — NPUs and GPU drivers materially change inference and rendering behavior.

Extension and plugin compatibility — reality vs expectations

This is where the ecosystem friction is most visible. Your extensions, password managers, and developer tools are often the deciding factor.

What we tested

Content blocking: uBlock‑style filter lists.
Password manager: Bitwarden auto-fill and browser integration.
Developer helper: a WebRequest-style HTTP inspector and a quick-access snippet runner.

Results

Chrome (Android): Historically Chrome for Android does not provide a full WebExtensions runtime; instead it integrates with Android Autofill and supports some native features. In our 2026 tests the browser maintained tight system integration for password managers (Autofill API) and supported site-level blocking via Play Store-installed ad-blockers, but it did not allow arbitrary desktop Chrome extensions to run.
Puma (Android): Puma shipped a plugin model optimized around on-device AI actions and content-blocking. It supported filter lists for adblocking and had an API for on-page AI actions (summarize, annotate). However, Puma’s plugin ecosystem is younger and does not support the full Chrome desktop extension catalog — expect gaps for niche developer extensions.

Enterprise implications

If you rely on specific Chrome desktop extensions (developer proxies, enterprise SSO helpers, advanced webRequest hooking), you will likely need a desktop policy or alternative mobile implementations. For security-sensitive users that need password managers, both browsers worked well with Android Autofill, which is the recommended cross-browser approach.

Actionable rollout checklist

Inventory critical extensions and map them to supported mobile alternatives (Autofill for passwords, native content-blocking lists for adblocking).
Test your SSO flows under each browser; some SAML/OIDC flows rely on browser behaviors that vary on Android.
For developer teams, provide a mobile debugging kit (remote Chrome DevTools, WebDriver scripts) and document known feature gaps if switching to Puma.

Security considerations and enterprise policy controls

Both browsers offer management hooks in 2026. Chrome benefits from mature enterprise policies; Puma provides a compact policy set focused on model residency and telemetry. Key recommendations:

Enforce egress filtering for cloud-model endpoints unless you explicitly allow them.
Require device attestation and secure boot on Pixel devices used for sensitive workloads.
Log and review every model download event — even local models were often downloaded once and cached.

Future trends to watch (2026 and beyond)

Quick predictions you need on your roadmap:

Standardized OS model APIs: Android and mobile vendors will publish consolidated NNAPI/WebML-like APIs for browser-hosted on-device models, reducing fragmentation.
Hybrid inference models: Expect more browsers to implement split-execution: small transformers on-device and large rationale stages in the cloud, controlled by enterprise policy. See work on edge orchestration patterns that enable split execution and regional caching.
Push for verified local models: Enterprises will demand signed model bundles and reproducible hashes to avoid supply-chain risks for on-device inference. Watch predictions from creator tooling and identity plays for related directionality: StreamLive Pro — 2026 Predictions.

Practical recommendations — which browser to pick for what use case

Privacy-first enterprise users (internal docs, PII): Puma with enforced local models and network egress rules. Advantages: data never leaves device by default; lower latency for short tasks.
Power users who need full extension ecosystem and maximum page performance: Chrome on managed Pixel devices with strict policy controls and telemetry auditing.
Mixed fleets: Consider hybrid policies: Puma for sensitive teams, Chrome for public-facing or developer teams, and provide a clear extension/feature mapping document.

How we recorded these tests — reproducible steps

Follow this minimal checklist to reproduce the main experiments:

Set up two Pixel devices with the latest Android build and enable developer options.
Install Puma and Chrome (latest stable). Disable auto-updates during the test window.
Install mitmproxy and add its CA to the device (note: some apps/browsers use pinning). Record traffic while browsing a neutral page.
Use adb to forward DevTools and run Lighthouse for each run (scripts above). Collect 10 runs per scenario; use the median.
For model tests: select Puma’s local model option and run the summarization tasks; measure timings with a stopwatch or devtools timing markers.
Document telemetry endpoints, model latency, and extension compatibility for each test run.

Limitations and caveats

No single test covers every configuration. Things that affect results:

Device model and NPU drivers — newer Pixel NPUs produce faster on-device inference.
Browser build differences — vendors push frequent updates that change behavior. Re-run tests before any major rollout.
Choice of local model — smaller models are faster but less knowledgeable.

Final takeaways

In 2026 the decision between Puma and Chrome on Pixel devices is not just about raw rendering speed; it's a tradeoff between privacy/residency and feature breadth. Puma brings a compelling local-first model that is faster for short, privacy-sensitive tasks and reduces telemetry exposure. Chrome continues to excel at complex page performance and provides a mature policy surface for enterprise management, at the cost of richer background services and server-assisted AI features.

Quick actionable checklist

Map sensitive workflows and decide whether on-device inference is required.
Run the reproducible tests above on your device fleet (use remote Lighthouse + mitmproxy).
Enforce egress policies and require consent for any cloud model use.
Use Android Autofill for password management to avoid extension compatibility issues.

Call to action

Want the raw test scripts, HAR captures, and Lighthouse traces we used? Grab the reproducible test kit and policy templates from our ops toolkit, or subscribe for a walkthrough webinar where we re-run these comparisons live on your target Pixel model. Make your browser choice predictable, measurable, and auditable — not a guess.

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