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BenchLM
Model A
Celeris-1

Celeris

—

Evidence status unavailable

90% interval unavailable

Celeris-1 vs DeepSeek V4 Pro 0813

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
DeepSeek V4 Pro 0813

DeepSeek

60.9/100

Estimated · Public rank #48

90% interval 51.1–70.8

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 based on different benchmark sets are marked directional and do not name a winner.

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

  • Chat turn cost

    1K fresh input + 500 output tokens

    Celeris-1

    Celeris-1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Celeris-1

    Celeris-1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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

What is actually comparable

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

Shared results
1
Celeris-1 only
0
DeepSeek V4 Pro 0813 only
33
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Directional only
Celeris-1
75.9
DeepSeek V4 Pro 0813
62.5
Weighted basis
1 vs 4 rows
Reading
Directional only

Agentic

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
74.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
70.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
95.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Celeris-1
Not measured
DeepSeek V4 Pro 0813
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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
Fits in one request
DeepSeek V4 Pro 0813
$0.00087
Fits in one request

Celeris-1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Celeris-1
$0.0121
Fits in one request
DeepSeek V4 Pro 0813
$0.02436
Fits in one request

Celeris-1 has the lower modeled cost

Costs use the listed standard API rates.

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
$0.01812
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.

Specification differences

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

Context window

Maximum documented context; output-token limits may be lower.

Celeris-1

128K

DeepSeek V4 Pro 0813

1M

DeepSeek models and pricing

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

DeepSeek V4 Pro 0813

$0.003625 per 1M cached input tokens

Reasoning profile

Celeris-1

Non-Reasoning

DeepSeek V4 Pro 0813

Reasoning

Weight access

Celeris-1

Proprietary

DeepSeek V4 Pro 0813

Proprietary

License

Celeris-1

Proprietary

DeepSeek V4 Pro 0813

Proprietary

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 $0.02436. Cache-heavy agent loop: $0.051 vs $0.01812.
Context tradeoff
DeepSeek V4 Pro 0813 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence34 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

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

Reasoning

  • 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

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

Math

  • 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

Frequently asked 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?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

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

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Celeris-1 or DeepSeek V4 Pro 0813?

For the stated presets, chat costs $0.00055 on Celeris-1 and $0.00087 on DeepSeek V4 Pro 0813; repository review costs $0.0121 and $0.02436; the cache-heavy agent loop costs $0.051 and $0.01812. 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 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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