Skip to contentT/TTokens or Towers

Compare

LeaderboardEvery tracked model, ranked and priced side by sideBenchmarksWhat each test measures and who leads itPricesAPI, media, and hardware rates with a cost calculator

Plan

Stack plannerSix questions to a priced local and hosted stackShortlistOur picks for subscriptions, APIs, and hardwareEnterpriseSeat plans against self-hosting for a whole team

Build

HardwareGPUs and workstations, and which models they fitHostsWhere to rent open-weight inference, and for how muchHarnessesCoding agents and the models they drive

Lab

FindingsResults from our own evaluationsScanAudit a repo’s AI usage in the browserMethodWhere the numbers come from and how we check them
Data checked September 25, 2026
Data checked September 25, 2026
Plan a stack
T/TTokens or Towers

An independent guide to AI models, what they cost, and the hardware to run them. No account, no API key, no sponsors.

Compare

  • Leaderboard
  • Benchmarks
  • Prices

Plan

  • Stack planner
  • Shortlist
  • Enterprise

Build

  • Hardware
  • Hosts
  • Harnesses

Lab

  • Findings
  • Scan
  • Method
Data checked September 25, 2026 · Every number links to its sourceRSS · Source on GitHub
Leaderboard/Google
Google

Google · Gemma 4

Gemma 4 E4B

Open weightsGenerally availableApache-2.0

On-device Gemma 4 (4.5B effective, 8B with embeddings) for phones and edge hardware.

AA Intelligence
8.9#106 of 142
Price
Self-host
Output speed
50tok/s
Context window
128K tokens

Composite indexes

  • Artificial Analysis Intelligence Index#106 of 142
    9independentv4.3.2, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗
    8independentv4.3.2, non-reasoningArtificial Analysis: Gemma 4 E4B (Non-reasoning) ↗

    Leader: Claude Opus 5.5 at 58

Coding

  • Terminal-Bench 2.x#100 of 103
    1.9%independentAA run (Terminal-Bench 2.1), reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: Claude Fable 5.1 at 91.4%

  • LiveCodeBench#33 of 56
    52%vendorLiveCodeBench v6Gemma 4 model card ↗

    Leader: Fugu at 92.9%

Agents and tool use

  • GDPval-AA#98 of 103
    -51independentGDPval-AA v2.1, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: Claude Opus 5.5 at 1846

  • τ²-Bench#66 of 88
    42.2%vendoraverage over 3 domainsGemma 4 model card ↗
    20.8%independentAA run, telecom, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: GLM-5.2 at 99.1%

Reasoning and knowledge

  • GPQA Diamond#114 of 142
    58.6%vendorGemma 4 model card ↗
    57.6%independentAA run, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: GPT-6 Astra at 96.1%

  • Humanity’s Last Exam#141 of 147
    3.8%independentAA run, no tools, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: Claude Opus 5.5 at 61.4%

  • CritPt#74 of 139
    0.6%independentAA run, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: GPT-5.6 Sol at 32.3%

  • MMLU-Pro#12 of 14
    69.4%vendorinstruction-tunedGemma 4 model card ↗

    Leader: Nemotron 3 Ultra 550B A55B at 86.8%

  • AIME 2026#12 of 13
    42.5%vendorno toolsGemma 4 model card ↗

    Leader: ERNIE 5.1 at 99.6%

Vision and long context

  • AA Long Context Reasoning#105 of 139
    32%independentAA-LCR v1.1, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: Kimi K3 at 88.7%

  • MMMU-Pro#64 of 72
    52.6%vendorGemma 4 model card ↗
    51.4%independentAA run, reasoningArtificial Analysis: Gemma 4 E4B (Reasoning) ↗

    Leader: Claude Opus 5.5 at 87.7%

Other published results

ResultScoreReported bySource
MRCR v2 (8-needle) · 128k average25.4%vendorGemma 4 model card ↗
MMMLU76.6%vendorGemma 4 model card ↗
Codeforces Elo940 ElovendorGemma 4 model card ↗

Specs

Released
March 31, 2026
Parameters
8B total, 4.5B active
Size class
Under 10B
Input
text, image, audio, video
Output
text
Context
128K tokens
Max output
Not published
First token
0.85s
  • Speed: Artificial Analysis: Gemma 4 E4B (Reasoning) (reasoning; median across third-party hosts) ↗

Will it fit your budget?

The planner prices this model against local hardware and other APIs for your workload.

Plan a stack →

Compare with

  • NVIDIANemotron 3 Nano 30B A3B8.9
  • OpenAIgpt-oss-20b9
  • Sarvam AISarvam 105B8.8
  • IBMGranite 4.2 3B9.1