Model comparison
Celeris-1 vs Qwen3.6 Plus
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); Qwen3.6 Plus #30 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Celeris-1 and Qwen3.6 Plus share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 59 to Qwen3.6 Plus.
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- Qwen3.6 Plus only
- 59
- 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; Qwen3.6 Plus is the better fit if you need the larger 1M context window or you want the stronger reasoning-first profile.
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 Qwen3.6 Plus 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.
Qwen3.6 Plus 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. Qwen3.6 Plus gives you the larger context window at 1M, 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 | Qwen3.6 Plus |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 18.8 | Qwen3.6 Plus57.1 |
| Agentic | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus61.6 |
| Coding | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus70.3 |
| Reasoning | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus62.0 |
| Math | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus60.5 |
| Multilingual | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus84.7 |
| Multimodal | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus79.8 |
| Inst. Following | Celeris-1Not measured | MarginNo overlap | Qwen3.6 Plus82.3 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 88.5%Winner: Qwen3.6 PlusΔ 12.6MMLU-Pro: Celeris-1 scored 75.9%; Qwen3.6 Plus scored 88.5%. Qwen3.6 Plus wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | Qwen3.6 Plus | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | Qwen3.6 PlusNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Celeris-1Not available | Qwen3.6 PlusNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | Qwen3.6 PlusNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | Qwen3.6 Plus1M | Qwen3.6 Plus lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | Celeris-1 | Qwen3.6 Plus | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 61.6% | Not comparable |
| Claw-EvalSource | — | 58.8% | Not comparable |
| QwenClawBenchSource | — | 57.2% | Not comparable |
| τ³-bench resultsSource | — | 70.7% | Not comparable |
| VITA-BenchSource | — | 44.3% | Not comparable |
| DeepPlanningSource | — | 41.5% | Not comparable |
| ToolathlonSource | — | 39.8% | Not comparable |
| MCP AtlasSource | — | 48.2% | Not comparable |
| MCP-TasksSource | — | 74.1% | Not comparable |
| WideResearchSource | — | 74.3% | Not comparable |
| AA Agentic IndexSource | — | 27.6% | Not comparable |
| τ²-bench resultsSource | — | 97.7% | Not comparable |
| GDPval-AASource | — | 31.8% | Not comparable |
| GDPval-AASource | — | 1135 | Not comparable |
| Gert LabsSource | — | 50.60% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
Coding7 benchmarks
| Benchmark | Celeris-1 | Qwen3.6 Plus | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | — | 78.8% | Not comparable |
| SWE-bench ProSource | — | 56.6% | Not comparable |
| SWE MultilingualSource | — | 73.8% | Not comparable |
| LiveCodeBench v6Source | — | 87.1% | Not comparable |
| Vibe Code BenchSource | — | 25.56% | Not comparable |
| AA Coding IndexSource | — | 54.5% | Not comparable |
| AA-SciCodeSource | — | 40.7% | Not comparable |
Reasoning4 benchmarks
KnowledgeCeleris-1 wins12 benchmarks
| Benchmark | Celeris-1 | Qwen3.6 Plus | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | 88.5% | Qwen3.6 Plus leads |
| GPQASource | — | 90.4% | Not comparable |
| SuperGPQASource | — | 71.6% | Not comparable |
| MMLU-ReduxSource | — | 94.5% | Not comparable |
| C-EvalSource | — | 93.3% | Not comparable |
| HLESource | — | 28.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 39.6% | Not comparable |
| AA-GPQA DiamondSource | — | 88.2% | Not comparable |
| AA-HLESource | — | 25.7% | Not comparable |
| AA-Omniscience IndexSource | — | 2.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.2% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 32.0% | Not comparable |
Math7 benchmarks
| Benchmark | Celeris-1 | Qwen3.6 Plus | Result |
|---|---|---|---|
| AIME26Source | — | 95.3% | Not comparable |
| HMMT Feb 2025Source | — | 96.7% | Not comparable |
| HMMT Nov 2025Source | — | 94.6% | Not comparable |
| HMMT Feb 2026Source | — | 87.8% | Not comparable |
| MMAnswerBenchSource | — | 83.8% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 26.207% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 8.333% | Not comparable |
Multilingual2 benchmarks
Multimodal9 benchmarks
| Benchmark | Celeris-1 | Qwen3.6 Plus | Result |
|---|---|---|---|
| MMMUSource | — | 86.0% | Not comparable |
| MMMU-ProSource | — | 78.8% | Not comparable |
| MathVisionSource | — | 88.0% | Not comparable |
| VideoMMMUSource | — | 84.0% | Not comparable |
| ScreenSpot ProSource | — | 68.2% | Not comparable |
| CharXivSource | — | 81.5% | Not comparable |
| V*Source | — | 96.9% | Not comparable |
| AA-MMMU-ProSource | — | 78.0% | Not comparable |
| Design Arena WebsiteSource | — | 1249 | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or Qwen3.6 Plus?
Celeris-1 and Qwen3.6 Plus 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 Qwen3.6 Plus?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 57.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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