Instruction following
Like-for-like- Agents-A1
- 94.8
- DeepSeek V3
- 86.1
- Weighted basis
- 1 vs 1 rows
- Reading
- Agents-A1 leads
Agents-A1 vs DeepSeek V3
Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Agents-A1 has the higher public score estimate, 61.25 versus 44.22, but the 90% score intervals overlap. Treat that as a lead, not a settled 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
Agents-A1
Agents-A1 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
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
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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. DeepSeek V3 does not fit this workload in one request. Agents-A1 has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
5 Agents-A1 only
1 Shared
5 DeepSeek V3 only
- Shared results
- 1
- Agents-A1 only
- 5
- DeepSeek V3 only
- 5
- Like-for-like categories
- 1 / 8
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.
Agentic
Not comparable- Agents-A1
- 75.5
- DeepSeek V3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- Agents-A1
- Not measured
- DeepSeek V3
- 38.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Agents-A1
- 60.2
- DeepSeek V3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Agents-A1
- 47.6
- DeepSeek V3
- 72.7
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- Agents-A1
- Not measured
- DeepSeek V3
- 1.7
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Agents-A1
- Not measured
- DeepSeek V3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Agents-A1
- Not measured
- DeepSeek V3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Agents-A1 | DeepSeek V3 | Weighted basis | Reading |
|---|---|---|---|---|
| Instruction following | 94.8 | 86.1 | Like-for-like1 vs 1 rows | Agents-A1 leads |
| Agentic | 75.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Coding | Not measured | 38.9 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | 60.2 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 47.6 | 72.7 | Not comparable1 vs 2 rows | Not comparable |
| Math | Not measured | 1.7 | Not comparable0 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | 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.
IFEval
Instruction following
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
- Agents-A1
- API rate not published
- Fits in one request
- DeepSeek V3
- $0.00082
- Fits in one request
Agents-A1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- DeepSeek V3
- $0.0168
- Fits in one request
Agents-A1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- DeepSeek V3
- $0.0304
- Does not fit in one request
DeepSeek V3 does not fit this workload in one request. Agents-A1 has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationAgents-A1DeepSeek V3
Context window
Maximum documented context; output-token limits may be lower.
Agents-A1
262K
DeepSeek V3
128K
API model ID
Agents-A1
Not sourced
DeepSeek V3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Agents-A1
No comparable hosted API rate
DeepSeek V3
$0.07 per 1M cached input tokens
Documented inputs
Agents-A1
Not sourced
DeepSeek V3
Not sourced
Documented outputs
Agents-A1
Not sourced
DeepSeek V3
Not sourced
Provider availability
Agents-A1
Not sourced
DeepSeek V3
Not sourced
Reasoning profile
Agents-A1
Reasoning
DeepSeek V3
Non-Reasoning
Weight access
Agents-A1
Open Weight
DeepSeek V3
Open Weight
License
Agents-A1
Open Weight
DeepSeek V3
Open Weight
Release date
Agents-A1
2026-06-26
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
- Agents-A1 has the higher public score estimate, 61.25 versus 44.22, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Agents-A1 has the larger documented window (262K).
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.
Agents-A1
API / mo$0
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.
Frequently asked questions
Which is better, Agents-A1 or DeepSeek V3?
Agents-A1 has the higher public score estimate, 61.25 versus 44.22, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Agents-A1 or DeepSeek V3?
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, Agents-A1 or DeepSeek V3?
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, Agents-A1 or DeepSeek V3?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Agents-A1 or DeepSeek V3?
Agents-A1 has the larger documented context window: 262K, compared with 128K.
Related comparisons
Last updated August 21, 2026
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