Knowledge
Directional only- GPT-4.1 nano
- 50.3
- Inkling
- 51.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
GPT-4.1 nano vs Inkling
Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Inkling has the higher public score, 67.02 versus 42.43, and the 90% score intervals do not overlap.
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.
Chat turn cost
1K fresh input + 500 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
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.
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 GPT-4.1 nano only
1 Shared
14 Inkling only
- Shared results
- 1
- GPT-4.1 nano only
- 3
- Inkling only
- 14
- 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.
Agentic
Not comparable- GPT-4.1 nano
- Not measured
- Inkling
- 69.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-4.1 nano
- Not measured
- Inkling
- 68.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1 nano
- Not measured
- Inkling
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 nano
- 1.0
- Inkling
- 97.1
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 nano
- Not measured
- Inkling
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 nano
- Not measured
- Inkling
- 76.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1 nano
- 83.2
- Inkling
- 79.8
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
| Category | GPT-4.1 nano | Inkling | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 50.3 | 51.6 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 69.4 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | 68.6 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 1.0 | 97.1 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 76.5 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | 83.2 | 79.8 | Not comparable1 vs 1 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.
GPQA
Knowledge
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
- Inkling
- $0.00421
- Fits in one request
GPT-4.1 nano 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
- Inkling
- $0.10754
- 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
- Inkling
- $0.159
- Fits in one request
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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-4.1 nanoInkling
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
Inkling
1M
API model ID
GPT-4.1 nano
Not sourced
Inkling
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
Inkling
$0.374 per 1M cached input tokens
Documented inputs
GPT-4.1 nano
Not sourced
Inkling
Not sourced
Documented outputs
GPT-4.1 nano
Not sourced
Inkling
Not sourced
Provider availability
GPT-4.1 nano
Not sourced
Inkling
Not sourced
Reasoning profile
GPT-4.1 nano
Non-Reasoning
Inkling
Hybrid
Weight access
GPT-4.1 nano
Proprietary
Inkling
Open Weight
License
GPT-4.1 nano
Proprietary
Inkling
Open Weight
Release date
GPT-4.1 nano
2025-04-14
Inkling
2026-07-15
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
- Inkling has the higher public score, 67.02 versus 42.43, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0062 vs $0.10754. Cache-heavy agent loop: $0.026 vs $0.159.
- Context tradeoff
- Both models list 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 evidence18 rows
Agentic
Coding
Knowledge
MMLU
GPT-4.1 nano80.1%SourceInkling—Not directly comparable
GPQA
GPT-4.1 nano50.3%SourceInkling87.9%SourceInkling leads this result
GPQA-D
GPT-4.1 nano—Inkling87.9%SourceNot directly comparable
HLE
GPT-4.1 nano—Inkling46%SourceNot directly comparable
HLE w/o tools
GPT-4.1 nano—Inkling30%SourceNot directly comparable
Math
Multimodal
Instruction following
Frequently asked questions
Which is better, GPT-4.1 nano or Inkling?
Inkling has the higher public score, 67.02 versus 42.43, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-4.1 nano or Inkling?
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 Inkling?
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 Inkling?
For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.00421 on Inkling; repository review costs $0.0062 and $0.10754; the cache-heavy agent loop costs $0.026 and $0.159. GPT-4.1 nano 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 Inkling?
Both models list the same context window, 1M.
Related comparisons
Last updated August 29, 2026
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