Model comparison
Celeris-1 vs Gemma 4 E4B
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Celeris-1 unranked (Not scored); Gemma 4 E4B #155 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and Gemma 4 E4B share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 13 to Gemma 4 E4B.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- Gemma 4 E4B only
- 13
- Comparable categories
- 1 / 8
Treat this as a split decision. Celeris-1 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Gemma 4 E4B is the better fit if you want the cheaper token bill or you need the larger 128K context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Celeris-1 and Gemma 4 E4B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Celeris-1 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E4B. That is roughly Infinityx on output cost alone. Gemma 4 E4B is the reasoning model in the pair, while Celeris-1 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Gemma 4 E4B gives you the larger context window at 128K, compared with 8K for Celeris-1.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Celeris-1 | Δ | Gemma 4 E4B |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 8.5 | Gemma 4 E4B67.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 69.4%Winner: Celeris-1Δ 6.5MMLU-Pro: Celeris-1 scored 75.9%; Gemma 4 E4B scored 69.4%. Celeris-1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | Gemma 4 E4B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | Gemma 4 E4B$0 input / $0 output | Gemma 4 E4B has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | Gemma 4 E4BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | Gemma 4 E4BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | Gemma 4 E4B128K | Gemma 4 E4B lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Celeris-1 | Gemma 4 E4B | Result |
|---|---|---|---|
| τ²-bench resultsSource | — | 20.8% | Not comparable |
Coding1 benchmarks
| Benchmark | Celeris-1 | Gemma 4 E4B | Result |
|---|---|---|---|
| AA-SciCodeSource | — | 24.4% | Not comparable |
Reasoning2 benchmarks
KnowledgeCeleris-1 wins8 benchmarks
| Benchmark | Celeris-1 | Gemma 4 E4B | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | 69.4% | Celeris-1 leads |
| GPQASource | — | 58.6% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 12.5% | Not comparable |
| AA-GPQA DiamondSource | — | 57.6% | Not comparable |
| AA-HLESource | — | 3.7% | Not comparable |
| AA-Omniscience IndexSource | — | -20.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 8.6% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 31.3% | Not comparable |
Multimodal1 benchmarks
| Benchmark | Celeris-1 | Gemma 4 E4B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | — | 51.4% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Celeris-1 | Gemma 4 E4B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 44.2% | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or Gemma 4 E4B?
Celeris-1 and Gemma 4 E4B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, Celeris-1 or Gemma 4 E4B?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 67.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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