Agentic
Not comparable- Claude 4 Sonnet
- Not measured
- Ministral 3 14B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
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.
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.
Prompts that approach the documented context limit
Ministral 3 14B
Ministral 3 14B has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Ministral 3 14B
Ministral 3 14B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Ministral 3 14B
Ministral 3 14B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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. Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Ministral 3 14B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
| Category | Claude 4 Sonnet | Ministral 3 14B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | 72.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 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 | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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.
1K fresh input + 500 output tokens
Ministral 3 14B has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Ministral 3 14B has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Ministral 3 14B has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude 4 Sonnet
200K
Ministral 3 14B
256K
Claude 4 Sonnet
Not sourced
Ministral 3 14B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude 4 Sonnet
Not published
Ministral 3 14B
Not published
Claude 4 Sonnet
Not sourced
Ministral 3 14B
Not sourced
Claude 4 Sonnet
Not sourced
Ministral 3 14B
Not sourced
Claude 4 Sonnet
Not sourced
Ministral 3 14B
Not sourced
Claude 4 Sonnet
Non-Reasoning
Ministral 3 14B
Non-Reasoning
Claude 4 Sonnet
Proprietary
Ministral 3 14B
Open Weight
Claude 4 Sonnet
Proprietary
Ministral 3 14B
Open Weight
Claude 4 Sonnet
2025-05-01
Ministral 3 14B
2025-12-02
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
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.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
For the stated presets, chat costs $0.0105 on Claude 4 Sonnet and $0.0003 on Ministral 3 14B; repository review costs $0.195 and $0.0106; the cache-heavy agent loop costs $0.81 and $0.046. Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Ministral 3 14B has no published cached-input rate, so cached tokens use its listed input rate.
Ministral 3 14B has the larger documented context window: 256K, compared with 200K.
Last updated July 28, 2026
One weekly note on benchmark changes, pricing moves, and models worth re-testing.