Updated October 29, 2026. Rank says Inkling-Small is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Inkling-Small has the higher public score estimate, 49.5 versus 47.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 10 results are shared. Category rows resting on Estimated evidence or 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-Small
Inkling-Small has the higher public coding point estimate, 36.9 to 34.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Inkling-Small
Inkling-Small has the higher public agentic point estimate, 36.1 to 33.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Inkling-Small
Inkling-Small has the larger documented context window.
1K fresh input + 500 output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
Inkling-Small has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
HLEKnowledge
Normalized gap 19.8HMMT Feb 2026Math
Normalized gap 17.5LiveCodeBench (Vals)Coding
Normalized gap 6.0AIME26Math
Normalized gap 5.7MMLU-Pro (Vals)Knowledge
Normalized gap 5.2Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | Inkling-Small | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 36.1Supported · #86/135 | 33.2Supported · #99/135 | Like-for-likeBenchAlign v5.8 lane · 5 vs 8 public rows | Inkling-Small leads · intervals overlap |
| Coding | 36.9Supported · #77/156 | 34.7Supported · #85/156 | Like-for-likeBenchAlign v5.8 lane · 6 vs 12 public rows | Inkling-Small leads · intervals overlap |
| Knowledge | 50.6Supported · #75/179 | 45.2Supported · #94/179 | Like-for-likeBenchAlign v5.8 lane · 6 vs 5 public rows | Inkling-Small leads · intervals overlap |
| Instruction following | 93.3#10/157 | 93.0#14/157 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 55.0Unranked · 4 rankable rows | 78.8Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 67.3#41/58 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 69.8#5/12 | 70.5Unranked · 4 rankable rows | Not comparableProvisional lane · 4 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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.
Inkling-Small
1M
MiniMax M2.7
200K
Inkling-Small
Not sourced
MiniMax M2.7
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Inkling-Small
$0.116 per 1M cached input tokens
MiniMax M2.7
Not published
Inkling-Small
Not sourced
MiniMax M2.7
Not sourced
Inkling-Small
Not sourced
MiniMax M2.7
Not sourced
Inkling-Small
Not sourced
MiniMax M2.7
Not sourced
Inkling-Small
Hybrid
MiniMax M2.7
Non-Reasoning
Inkling-Small
Open Weight
MiniMax M2.7
Open Weight
Inkling-Small
Open Weight
MiniMax M2.7
Open Weight
Inkling-Small
2026-07-30
MiniMax M2.7
2026-03-18
Inkling-Small has the higher public score estimate, 49.5 versus 47.29, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Inkling-Small has the higher public coding point estimate, 36.9 to 34.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Inkling-Small has the higher public agentic tasks point estimate, 36.1 to 33.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0013 on Inkling-Small and $0.0009 on MiniMax M2.7; repository review costs $0.03332 and $0.0186; the cache-heavy agent loop costs $0.0492 and $0.078. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Inkling-Small has the larger documented context window: 1M, compared with 200K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Inkling-Small leads this result
Terminal-Bench 2.0
Not directly comparable
τ³-bench results
Not directly comparable
Toolathlon
Not directly comparable
MLE-Bench Lite
Not directly comparable
MM-ClawBench
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
MiniMax M2.7 leads this result
Terminal-Bench 2.1
Not directly comparable
SciCode
Not directly comparable
LiveCodeBench (Vals)
Inkling-Small leads this result
SWE-bench (Vals)
Inkling-Small leads this result
SWE-bench Verified*
Not directly comparable
SWE-Rebench
Not directly comparable
SWE Multilingual
Not directly comparable
Multi-SWE Bench
Not directly comparable
VIBE-Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Inkling-Small leads this result
HLE
Inkling-Small leads this result
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
MiniMax M2.7 leads this result
MMLU-Pro (Vals)
Inkling-Small leads this result
MMLU-Pro (Arcee)
Not directly comparable
IFBench
Not directly comparable
AIME26
Inkling-Small leads this result
HMMT Feb 2026
Inkling-Small leads this result
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME25 (Arcee)
Not directly comparable
HMMT Nov 2025
Not directly comparable
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Last updated October 29, 2026
