Coding
Like-for-like- Inkling
- 68.6
- MiniMax M3
- 72.2
- Weighted basis
- 2 vs 2 rows
- Reading
- MiniMax M3 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
MiniMax M3 has the higher public score estimate, 68.73 versus 67.02, 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
MiniMax M3
MiniMax M3 leads on the same 2 weighted benchmark rows.
Confidence: limited
1K fresh input + 500 output tokens
MiniMax M3
MiniMax M3 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
MiniMax M3
MiniMax M3 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
MiniMax M3
MiniMax M3 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
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories use 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 | Inkling | MiniMax M3 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 68.6 | 72.2 | Like-for-like2 vs 2 rows | MiniMax M3 leads |
| Agentic | 69.4 | 72.3 | Directional only2 vs 3 rows | Directional only |
| Multimodal | 76.5 | 64.9 | Directional only2 vs 2 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 51.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Math | 97.1 | 85.7 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 79.8 | Not measured | Not comparable1 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.
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.
BrowseComp
Agentic
SWE-bench Pro
Coding
MMMU-Pro
Multimodal
SWE-bench Verified
Coding
Terminal-Bench 2.0
Agentic
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 M3 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
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
1M
MiniMax M3
1M
Inkling
Not sourced
MiniMax M3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Inkling
$0.374 per 1M cached input tokens
MiniMax M3
$0.06 per 1M cached input tokens
Inkling
Not sourced
MiniMax M3
Not sourced
Inkling
Not sourced
MiniMax M3
Not sourced
Inkling
Not sourced
MiniMax M3
Not sourced
Inkling
Hybrid
MiniMax M3
Non-Reasoning
Inkling
Open Weight
MiniMax M3
Open Weight
Inkling
Open Weight
MiniMax M3
Open Weight
Inkling
2026-07-15
MiniMax M3
2026-06-01
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
MiniMax M3 leads this result
BrowseComp
MiniMax M3 leads this result
MCP Atlas
MiniMax M3 leads this result
OSWorld-Verified
Not directly comparable
Claw-Eval
Not directly comparable
BankerToolBench
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
SWE-bench Verified
MiniMax M3 leads this result
SWE-bench Pro
MiniMax M3 leads this result
Terminal-Bench 2.0
MiniMax M3 leads this result
NL2Repo
Not directly comparable
VIBE V2
Not directly comparable
SVG-Bench
Not directly comparable
KernelBench Hard
Not directly comparable
OpenHarmony Bench
Not directly comparable
MMMU-Pro
MiniMax M3 leads this result
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
OfficeQA Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
VideoMMMU
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
IFBench
Not directly comparable
MiniMax M3 has the higher public score estimate, 68.73 versus 67.02, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
MiniMax M3 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 $0.00421 on Inkling and $0.0009 on MiniMax M3; repository review costs $0.10754 and $0.0186; the cache-heavy agent loop costs $0.159 and $0.03. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Last updated August 29, 2026
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