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BenchLM

Celeris-1 vs DeepSeek V4 Pro 0813

Updated September 27, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Celeris logo
Celeris-1

Celeris

—

Evidence status unavailable

90% interval unavailable

Model B
DeepSeek logo
DeepSeek V4 Pro 0813

DeepSeek

63.48/100

Estimated · Public rank #33

90% interval 52.0–75.0

Shared results
1
Celeris-1 only
3
DeepSeek V4 Pro 0813 only
41
Like-for-like categories
0 / 8
Estimated: DeepSeek V4 Pro 0813How the comparison works

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 V4 Pro 0813

    DeepSeek V4 Pro 0813 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. 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—Celeris-150.3DeepSeek V4 Pro 0813

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Agentic

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
55.0
Supported · #29/105
Basis
BenchAlign v5.7 lane · 0 vs 11 public rows
Reading
Not comparable

Coding

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
50.3
Supported · #39/135
Basis
BenchAlign v5.7 lane · 0 vs 15 public rows
Reading
Not comparable

Reasoning

Not comparable
Celeris-1
48.2
Unranked · 3 rankable rows
DeepSeek V4 Pro 0813
56.9
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
63.4
Estimated · #29/158
Basis
BenchAlign v5.7 lane · 1 vs 8 public rows
Reading
Not comparable

Multilingual

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Celeris-1
Not ranked
DeepSeek V4 Pro 0813
80.2
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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 V4 Pro 0813
$0.0033
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 V4 Pro 0813
$0.07788
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 V4 Pro 0813
$0.0748
Fits in one request

Celeris-1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

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 pricing

DeepSeek V4 Pro 0813

$0.044 per 1M cached input tokens

DeepSeek: Models & Pricing

Reasoning profile

Celeris-1

Non-Reasoning

DeepSeek V4 Pro 0813

Reasoning

Weight access

Celeris-1

Proprietary

DeepSeek V4 Pro 0813

Open Weight

License

Celeris-1

Proprietary

DeepSeek V4 Pro 0813

Open Weight

Release date

Celeris-1

2026-07-22

DeepSeek V4 Pro 0813

2026-08-13

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 $0.07788. Cache-heavy agent loop: $0.051 vs $0.0748.
Context tradeoff
DeepSeek V4 Pro 0813 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Celeris-1 or DeepSeek V4 Pro 0813?

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 V4 Pro 0813?

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 V4 Pro 0813?

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 V4 Pro 0813?

For the stated presets, chat costs $0.00055 on Celeris-1 and $0.0033 on DeepSeek V4 Pro 0813; repository review costs $0.0121 and $0.07788; the cache-heavy agent loop costs $0.051 and $0.0748. Celeris-1 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 V4 Pro 0813?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 131,072 tokens.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence45 rows

Agentic

  • Terminal-Bench 2.0

    Celeris-1—
    DeepSeek V4 Pro 081367.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Celeris-1—
    DeepSeek V4 Pro 081387.9%
    Source

    Not directly comparable

  • BrowseComp

    Celeris-1—
    DeepSeek V4 Pro 081383.4%
    Source

    Not directly comparable

  • HLE w/ tools

    Celeris-1—
    DeepSeek V4 Pro 081360.0%
    Source

    Not directly comparable

  • MCP Atlas

    Celeris-1—
    DeepSeek V4 Pro 081373.6%
    Source

    Not directly comparable

  • Toolathlon

    Celeris-1—
    DeepSeek V4 Pro 081351.8%
    Source

    Not directly comparable

  • CyberGym

    Celeris-1—
    DeepSeek V4 Pro 081383.3%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Celeris-1—
    DeepSeek V4 Pro 081374.1%
    Source

    Not directly comparable

  • Agents' Last Exam

    Celeris-1—
    DeepSeek V4 Pro 081325.7%
    Source

    Not directly comparable

  • AutomationBench

    Celeris-1—
    DeepSeek V4 Pro 081331.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Celeris-1—
    DeepSeek V4 Pro 081354.7%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    Celeris-1—
    DeepSeek V4 Pro 081393.5%
    Source

    Not directly comparable

  • Codeforces

    Celeris-1—
    DeepSeek V4 Pro 08133206.0
    Source

    Not directly comparable

  • SWE-bench Verified

    Celeris-1—
    DeepSeek V4 Pro 081380.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Celeris-1—
    DeepSeek V4 Pro 081355.4%
    Source

    Not directly comparable

  • SWE Multilingual

    Celeris-1—
    DeepSeek V4 Pro 081376.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Celeris-1—
    DeepSeek V4 Pro 081367.9%
    Source

    Not directly comparable

  • Vibe Code Bench

    Celeris-1—
    DeepSeek V4 Pro 081349.93%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Celeris-1—
    DeepSeek V4 Pro 081387.9%
    Source

    Not directly comparable

  • NL2Repo

    Celeris-1—
    DeepSeek V4 Pro 081361.5%
    Source

    Not directly comparable

  • DeepSWE

    Celeris-1—
    DeepSeek V4 Pro 081362.7%
    Source

    Not directly comparable

  • DSBench-FullStack

    Celeris-1—
    DeepSeek V4 Pro 081371.1%
    Source

    Not directly comparable

  • DSBench-Hard

    Celeris-1—
    DeepSeek V4 Pro 081367.2%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Celeris-1—
    DeepSeek V4 Pro 081359.0%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Celeris-1—
    DeepSeek V4 Pro 081387.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Celeris-1—
    DeepSeek V4 Pro 081396.4%
    Source

    Not directly comparable

Reasoning

  • DROP

    Celeris-181.4%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

  • MRCR 1M

    Celeris-1—
    DeepSeek V4 Pro 081383.5%
    Source

    Not directly comparable

  • CorpusQA 1M

    Celeris-1—
    DeepSeek V4 Pro 081362.0%
    Source

    Not directly comparable

  • ARC-AGI-1

    Celeris-1—
    DeepSeek V4 Pro 081390.00%
    Source

    Not directly comparable

  • ARC-AGI-2

    Celeris-1—
    DeepSeek V4 Pro 081361.3%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    Celeris-175.9%
    Source
    DeepSeek V4 Pro 081387.5%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • SimpleQA

    Celeris-1—
    DeepSeek V4 Pro 081357.9%
    Source

    Not directly comparable

  • Chinese-SimpleQA

    Celeris-1—
    DeepSeek V4 Pro 081384.4%
    Source

    Not directly comparable

  • GPQA

    Celeris-1—
    DeepSeek V4 Pro 081390.1%
    Source

    Not directly comparable

  • GPQA-D

    Celeris-1—
    DeepSeek V4 Pro 081390.1%
    Source

    Not directly comparable

  • HLE

    Celeris-1—
    DeepSeek V4 Pro 081342.7%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Celeris-1—
    DeepSeek V4 Pro 081392.4%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Celeris-1—
    DeepSeek V4 Pro 081387.0%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Celeris-180.8%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

Math

  • GSM8K

    Celeris-193.7%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

  • HMMT Feb 2026

    Celeris-1—
    DeepSeek V4 Pro 081395.2%
    Source

    Not directly comparable

  • IMOAnswerBench

    Celeris-1—
    DeepSeek V4 Pro 081389.8%
    Source

    Not directly comparable

  • Apex

    Celeris-1—
    DeepSeek V4 Pro 081338.3%
    Source

    Not directly comparable

  • Apex Shortlist

    Celeris-1—
    DeepSeek V4 Pro 081390.2%
    Source

    Not directly comparable

45 public results · 1 shared

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Last updated September 27, 2026