Coding
Like-for-like- Grok 4.20
- 67.1
- Inkling
- 68.6
- Weighted basis
- 2 vs 2 rows
- Reading
- Inkling leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start the free Radar BriefUpdated 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 estimate, 67.02 versus 55.44, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
7 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.
Code generation, repair, and software-engineering tasks
Inkling
Inkling leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
Grok 4.20
Grok 4.20 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Inkling
Inkling has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Inkling
Inkling has the lower estimated token cost for this stated workload. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Inkling
Inkling has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 | Grok 4.20 | Inkling | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 67.1 | 68.6 | Like-for-like2 vs 2 rows | Inkling leads |
| Multimodal | 70.1 | 76.5 | Like-for-like2 vs 2 rows | Inkling leads |
| Agentic | 47.1 | 69.4 | Directional only1 vs 2 rows | Directional only |
| Reasoning | 53.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | Not measured | 51.6 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | 97.1 | Not comparable0 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | 79.8 | Not comparable0 vs 1 rows | Not comparable |
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.
CharXiv
Multimodal
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
MMMU-Pro
Multimodal
SWE-bench Verified
Coding
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
Inkling has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Inkling has the lower modeled cost
Grok 4.20 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.
Grok 4.20
2M
Inkling
1M
Grok 4.20
Not sourced
Inkling
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Grok 4.20
Not published
Inkling
$0.374 per 1M cached input tokens
Grok 4.20
Not sourced
Inkling
Not sourced
Grok 4.20
Not sourced
Inkling
Not sourced
Grok 4.20
Not sourced
Inkling
Not sourced
Grok 4.20
Reasoning
Inkling
Hybrid
Grok 4.20
Proprietary
Inkling
Open Weight
Grok 4.20
Proprietary
Inkling
Open Weight
Grok 4.20
2026-03-10
Inkling
2026-07-15
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.
Terminal-Bench 2.0
Inkling leads this result
DeepSearchQA
Not directly comparable
Gert Labs
Not directly comparable
BrowseComp
Not directly comparable
MCP Atlas
Not directly comparable
LiveCodeBench Pro
Not directly comparable
SWE-bench Verified
Inkling leads this result
SWE-bench Pro
Inkling leads this result
Vibe Code Bench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
GPQA-D
Grok 4.20 leads this result
HLE w/o tools
Grok 4.20 leads this result
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
GPQA
Not directly comparable
HLE
Not directly comparable
AIME26
Not directly comparable
MMMU-Pro
Grok 4.20 leads this result
CharXiv
Inkling leads this result
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
MedXpertQA (MM)
Not directly comparable
CharXiv w/o tools
Not directly comparable
IFBench
Not directly comparable
Inkling has the higher public score estimate, 67.02 versus 55.44, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Inkling leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.005 on Grok 4.20 and $0.00421 on Inkling; repository review costs $0.118 and $0.10754; the cache-heavy agent loop costs $0.5 and $0.159. Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate.
Grok 4.20 has the larger documented context window: 2M, compared with 1M.
Last updated August 29, 2026
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