Agentic
Not comparable- GPT-4.1 nano
- Not measured
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
GPT-4.1 nano vs Ministral 3 8B (Reasoning)
Updated July 28, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
GPT-4.1 nano
GPT-4.1 nano has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Ministral 3 8B (Reasoning)
Ministral 3 8B (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Ministral 3 8B (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-4.1 nano
GPT-4.1 nano 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 GPT-4.1 nano only
- Shared results
- 0
- GPT-4.1 nano only
- 4
- Ministral 3 8B (Reasoning) only
- 0
- Like-for-like categories
- 0 / 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.
Coding
Not comparable- GPT-4.1 nano
- Not measured
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1 nano
- Not measured
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4.1 nano
- 50.3
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 nano
- 1.0
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 nano
- Not measured
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 nano
- Not measured
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1 nano
- 83.2
- Ministral 3 8B (Reasoning)
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | GPT-4.1 nano | Ministral 3 8B (Reasoning) | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 50.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 1.0 | Not measured | Not comparable1 vs 0 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 |
| Instruction following | 83.2 | Not measured | Not comparable1 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.
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
- GPT-4.1 nano
- $0.0003
- Fits in one request
- Ministral 3 8B (Reasoning)
- $0.00022
- Fits in one request
Ministral 3 8B (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 nano
- $0.0062
- Fits in one request
- Ministral 3 8B (Reasoning)
- $0.00795
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4.1 nano
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
- Ministral 3 8B (Reasoning)
- $0.0345
- Fits in one request
- Cached input priced at the published list-input rate
GPT-4.1 nano has the lower modeled cost
GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Ministral 3 8B (Reasoning) 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.
SpecificationGPT-4.1 nanoMinistral 3 8B (Reasoning)
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
Ministral 3 8B (Reasoning)
256K
API model ID
GPT-4.1 nano
Not sourced
Ministral 3 8B (Reasoning)
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-4.1 nano
Not published
Ministral 3 8B (Reasoning)
Not published
Documented inputs
GPT-4.1 nano
Not sourced
Ministral 3 8B (Reasoning)
Not sourced
Documented outputs
GPT-4.1 nano
Not sourced
Ministral 3 8B (Reasoning)
Not sourced
Provider availability
GPT-4.1 nano
Not sourced
Ministral 3 8B (Reasoning)
Not sourced
Reasoning profile
GPT-4.1 nano
Non-Reasoning
Ministral 3 8B (Reasoning)
Reasoning
Weight access
GPT-4.1 nano
Proprietary
Ministral 3 8B (Reasoning)
Open Weight
License
GPT-4.1 nano
Proprietary
Ministral 3 8B (Reasoning)
Open Weight
Release date
GPT-4.1 nano
2025-04-14
Ministral 3 8B (Reasoning)
2025-12-02
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0062 vs $0.00795. Cache-heavy agent loop: $0.026 vs $0.0345.
- Context tradeoff
- GPT-4.1 nano 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.
Frequently asked questions
Which is better, GPT-4.1 nano or Ministral 3 8B (Reasoning)?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-4.1 nano or Ministral 3 8B (Reasoning)?
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, GPT-4.1 nano or Ministral 3 8B (Reasoning)?
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, GPT-4.1 nano or Ministral 3 8B (Reasoning)?
For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.00022 on Ministral 3 8B (Reasoning); repository review costs $0.0062 and $0.00795; the cache-heavy agent loop costs $0.026 and $0.0345. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Ministral 3 8B (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 nano or Ministral 3 8B (Reasoning)?
GPT-4.1 nano has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated July 28, 2026
Keep the decision current
One weekly note on benchmark changes, pricing moves, and models worth re-testing.