Model comparison
Celeris-1 vs DeepSeek V4 Pro (Max)
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Evidence parity. Celeris-1 and DeepSeek V4 Pro (Max) share 1 comparable benchmark result. 1 of 8 categories are comparable. 0 results are unique to Celeris-1; 47 to DeepSeek V4 Pro (Max).
Updated July 24, 2026- Shared results
- 1
- Celeris-1 only
- 0
- DeepSeek V4 Pro (Max) only
- 47
- 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; DeepSeek V4 Pro (Max) is the better fit if you want the cheaper token bill or you need the larger 1M 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 DeepSeek V4 Pro (Max) 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 $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro (Max). That is roughly 6.9x on output cost alone. DeepSeek V4 Pro (Max) 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. DeepSeek V4 Pro (Max) 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 | DeepSeek V4 Pro (Max) |
|---|---|---|---|
| Knowledge | Celeris-175.9 | Margin← 15.8 | DeepSeek V4 Pro (Max)60.1 |
| Agentic | Celeris-1Not measured | MarginNo overlap | DeepSeek V4 Pro (Max)74.5 |
| Coding | Celeris-1Not measured | MarginNo overlap | DeepSeek V4 Pro (Max)70.9 |
| Math | Celeris-1Not measured | MarginNo overlap | DeepSeek V4 Pro (Max)95.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 75.9%B 87.5%Winner: DeepSeek V4 Pro (Max)Δ 11.6MMLU-Pro: Celeris-1 scored 75.9%; DeepSeek V4 Pro (Max) scored 87.5%. DeepSeek V4 Pro (Max) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Celeris-1 | DeepSeek V4 Pro (Max) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Celeris-1$2 input / $6 output | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | DeepSeek V4 Pro (Max) has the lower combined listed price. |
| Generation speedtokens per second | Celeris-1Not available | DeepSeek V4 Pro (Max)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Celeris-1Not available | DeepSeek V4 Pro (Max)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Celeris-18K | DeepSeek V4 Pro (Max)1M | DeepSeek V4 Pro (Max) lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | Celeris-1 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 67.9% | Not comparable |
| BrowseCompSource | — | 83.4% | Not comparable |
| HLE w/ toolsSource | — | 48.2% | Not comparable |
| MCP AtlasSource | — | 73.6% | Not comparable |
| GDPval-AASource | — | 1307 | Not comparable |
| ToolathlonSource | — | 51.8% | Not comparable |
| AA Agentic IndexSource | — | 36.4% | Not comparable |
| APEX-Agents-AASource | — | 24.3% | Not comparable |
| τ²-bench resultsSource | — | 96.2% | Not comparable |
| GDPval-AASource | — | 40.4% | Not comparable |
| AA BriefcaseSource | — | 932 | Not comparable |
| AA EnterpriseOps-GymSource | — | 40.4% | Not comparable |
| AA Harvey LABSource | — | 84.4% | Not comparable |
| AA ITBenchSource | — | 38.3% | Not comparable |
| AA Tau3 BankingSource | — | 25.8% | Not comparable |
| terminalBenchHardSource | — | 46.2% | Not comparable |
| aaTerminalBench21Source | — | 64% | Not comparable |
Coding8 benchmarks
| Benchmark | Celeris-1 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| CodeforcesSource | — | 3206.0 | Not comparable |
| SWE-bench VerifiedSource | — | 80.6% | Not comparable |
| SWE-bench ProSource | — | 55.4% | Not comparable |
| SWE MultilingualSource | — | 76.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 67.9% | Not comparable |
| Vibe Code BenchSource | — | 49.93% | Not comparable |
| AA Coding IndexSource | — | 59.4% | Not comparable |
| AA-SciCodeSource | — | 50.0% | Not comparable |
Reasoning4 benchmarks
KnowledgeCeleris-1 wins13 benchmarks
| Benchmark | Celeris-1 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| MMLU-ProSource | 75.9% | 87.5% | DeepSeek V4 Pro (Max) leads |
| SimpleQASource | — | 57.9% | Not comparable |
| Chinese-SimpleQASource | — | 84.4% | Not comparable |
| GPQASource | — | 90.1% | Not comparable |
| GPQA-DSource | — | 90.1% | Not comparable |
| HLESource | — | 37.7% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 44.3% | Not comparable |
| AA-GPQA DiamondSource | — | 88.8% | Not comparable |
| AA-HLESource | — | 35.9% | Not comparable |
| AA-Omniscience IndexSource | — | -10.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 43.3% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 94.0% | Not comparable |
| AA Openness IndexSource | — | 50.0% | Not comparable |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | Celeris-1 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1264 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Celeris-1 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 76.5% | Not comparable |
Frequently Asked Questions (2)
Which is better, Celeris-1 or DeepSeek V4 Pro (Max)?
Celeris-1 and DeepSeek V4 Pro (Max) 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 DeepSeek V4 Pro (Max)?
Celeris-1 has the edge for knowledge tasks in this comparison, averaging 75.9 versus 60.1. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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