Agentic
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- 38.5
- Estimated · #124/151
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Celeris-1 vs DeepSeek V3
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
DeepSeek V3
DeepSeek V3 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Celeris-1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Celeris-1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. DeepSeek V3 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Celeris-1 only
2 Shared
4 DeepSeek V3 only
- Shared results
- 2
- Celeris-1 only
- 2
- DeepSeek V3 only
- 4
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- 41.1
- Estimated · #135/183
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Celeris-1
- 47.8
- Unranked · 3 rankable rows
- DeepSeek V3
- 43.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- 40.7
- Estimated · #136/181
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- 26.2
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Celeris-1
- Not ranked
- DeepSeek V3
- 39.6
- #102/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Celeris-1 | DeepSeek V3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 38.5Estimated · #124/151 | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Coding | Not ranked | 41.1Estimated · #135/183 | Not comparableBenchAlign lane · 0 vs 2 public rows | Not comparable |
| Reasoning | 47.8Unranked · 3 rankable rows | 43.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 40.7Estimated · #136/181 | Not comparableBenchAlign lane · 1 vs 2 public rows | Not comparable |
| Math | Not ranked | 26.2Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 39.6#102/120 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Celeris-1
- $0.00055
- Does not fit in one request
- DeepSeek V3
- $0.00082
- Fits in one request
Celeris-1 does not fit this workload in one request.
Repository review
50K fresh input + 3K output tokens
- Celeris-1
- $0.0121
- Does not fit in one request
- DeepSeek V3
- $0.0168
- Fits in one request
Celeris-1 does not fit this workload in one request.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Celeris-1
- $0.051
- Does not fit in one request
- Cached input priced at the published list-input rate
- DeepSeek V3
- $0.0304
- Does not fit in one request
Celeris-1 does not fit this workload in one request. DeepSeek V3 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationCeleris-1DeepSeek V3
Context window
Maximum documented context; output-token limits may be lower.
Celeris-1
131,072 tokens
Celeris-1 model guideDeepSeek V3
128K
API model ID
Celeris-1
celeris-1
Celeris-1 model guideDeepSeek V3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Celeris-1
Not published
Celeris API pricingDeepSeek V3
$0.07 per 1M cached input tokens
Documented inputs
Celeris-1
text, image
Celeris-1 model guideDeepSeek V3
Not sourced
Documented outputs
Celeris-1
DeepSeek V3
Not sourced
Provider availability
Celeris-1
Generally Available · Celeris OpenAI-compatible API (United States)
Celeris availabilityDeepSeek V3
Not sourced
Reasoning profile
Celeris-1
Non-Reasoning
DeepSeek V3
Non-Reasoning
Weight access
Celeris-1
Proprietary
DeepSeek V3
Open Weight
License
Celeris-1
Proprietary
DeepSeek V3
Open Weight
Release date
Celeris-1
2026-07-22
DeepSeek V3
2024-12-26
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0121 vs $0.0168. Cache-heavy agent loop: $0.051 vs $0.0304.
- Context tradeoff
- DeepSeek V3 has the larger documented window (128K).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Celeris-1
API / mo$675
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence8 rows
Coding
Reasoning
DROP
Celeris-181.4%SourceDeepSeek V3—Not directly comparable
Knowledge
Math
Instruction following
Frequently asked questions
Which is better, Celeris-1 or DeepSeek V3?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Celeris-1 or DeepSeek V3?
Celeris-1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Celeris-1 or DeepSeek V3?
Celeris-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Celeris-1 or DeepSeek V3?
For the stated presets, chat costs $0.00055 on Celeris-1 and $0.00082 on DeepSeek V3; repository review costs $0.0121 and $0.0168; the cache-heavy agent loop costs $0.051 and $0.0304. Celeris-1 does not fit this workload in one request. DeepSeek V3 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Celeris-1 or DeepSeek V3?
DeepSeek V3 has the larger documented context window: 128K, compared with 131,072 tokens.
Related comparisons
Last updated September 4, 2026
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