Agentic
Directional only- GPT-5.6 Sol
- 92.0
- MiniMax M3
- 72.3
- Weighted basis
- 2 vs 3 rows
- Reading
- Directional only
Model comparison
GPT-5.6 Sol vs MiniMax M3
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Sol has the higher public score, 81.48 versus 68.78, and the 90% score intervals do not overlap.
6 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.
Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
Chat turn cost
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
Cache-heavy agent loop cost
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
Show secondary and unsupported calls
Repository review cost
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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
18 GPT-5.6 Sol only
6 Shared
15 MiniMax M3 only
- Shared results
- 6
- GPT-5.6 Sol only
- 18
- MiniMax M3 only
- 15
- Like-for-like categories
- 0 / 8
3 categories use 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.
Coding
Directional only- GPT-5.6 Sol
- 64.6
- MiniMax M3
- 72.2
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.6 Sol
- 83.0
- MiniMax M3
- 64.9
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Sol
- 92.5
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Sol
- 94.6
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 87.5
- MiniMax M3
- 85.7
- Weighted basis
- 2 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | MiniMax M3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 92.0 | 72.3 | Directional only2 vs 3 rows | Directional only |
| Coding | 64.6 | 72.2 | Directional only1 vs 2 rows | Directional only |
| Multimodal | 83.0 | 64.9 | Directional only1 vs 2 rows | Directional only |
| Reasoning | 92.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 94.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 87.5 | 85.7 | Not comparable2 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 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.
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
SWE-bench Pro
Coding
MMMU-Pro
Multimodal
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-5.6 Sol
- $0.02
- Fits in one request
- MiniMax M3
- $0.0009
- Fits in one request
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Sol
- $0.34
- Fits in one request
- MiniMax M3
- $0.0186
- Fits in one request
MiniMax M3 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-5.6 Sol
- $0.5
- Fits in one request
- MiniMax M3
- $0.03
- Fits in one request
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 SolMiniMax M3
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogMiniMax M3
1M
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogMiniMax M3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingMiniMax M3
$0.06 per 1M cached input tokens
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogMiniMax M3
Not sourced
Documented outputs
GPT-5.6 Sol
MiniMax M3
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogMiniMax M3
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
MiniMax M3
Non-Reasoning
Weight access
GPT-5.6 Sol
Proprietary
MiniMax M3
Open Weight
License
GPT-5.6 Sol
Proprietary
MiniMax M3
Open Weight
Release date
GPT-5.6 Sol
2026-07-09
MiniMax M3
2026-06-01
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
- GPT-5.6 Sol has the higher public score, 81.48 versus 68.78, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.34 vs $0.0186. Cache-heavy agent loop: $0.5 vs $0.03.
- Context tradeoff
- GPT-5.6 Sol has the larger documented window (1.05M).
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 evidence39 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceMiniMax M366%SourceGPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%SourceMiniMax M383.5%SourceGPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%SourceMiniMax M34.6%SourceGPT-5.6 Sol leads this result
CyberGym
GPT-5.6 Sol84.5%SourceMiniMax M3—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceMiniMax M3—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceMiniMax M3—Not directly comparable
OSWorld-Verified
GPT-5.6 Sol—MiniMax M370.1%SourceNot directly comparable
MCP Atlas
GPT-5.6 Sol—MiniMax M374.2%SourceNot directly comparable
Claw-Eval
GPT-5.6 Sol—MiniMax M374.5%SourceNot directly comparable
BankerToolBench
GPT-5.6 Sol—MiniMax M376.1%SourceNot directly comparable
ResearchClawBench
GPT-5.6 Sol—MiniMax M319.8%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Sol64.6%SourceMiniMax M359%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceMiniMax M366.0%SourceGPT-5.6 Sol leads this result
deepSwe
GPT-5.6 Sol72.7%SourceMiniMax M3—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceMiniMax M3—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceMiniMax M3—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceMiniMax M3—Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—MiniMax M380.5%SourceNot directly comparable
NL2Repo
GPT-5.6 Sol—MiniMax M342.1%SourceNot directly comparable
VIBE V2
GPT-5.6 Sol—MiniMax M350.1%SourceNot directly comparable
SVG-Bench
GPT-5.6 Sol—MiniMax M363.7%SourceNot directly comparable
KernelBench Hard
GPT-5.6 Sol—MiniMax M328.8%SourceNot directly comparable
Reasoning
Knowledge
Math
FrontierMath (legacy)
GPT-5.6 Sol89%SourceMiniMax M3—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%SourceMiniMax M3—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%SourceMiniMax M3—Not directly comparable
USAMO 2026
GPT-5.6 Sol—MiniMax M385.7%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.6 Sol83%SourceMiniMax M378.1%SourceGPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%SourceMiniMax M3—Not directly comparable
OfficeQA Pro
GPT-5.6 Sol—MiniMax M345.1%SourceNot directly comparable
OmniDocBench 1.5
GPT-5.6 Sol—MiniMax M391.6%SourceNot directly comparable
VideoMMMU
GPT-5.6 Sol—MiniMax M384.6%SourceNot directly comparable
Video-MME (with subtitle)
GPT-5.6 Sol—MiniMax M385.4%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Sol or MiniMax M3?
GPT-5.6 Sol has the higher public score, 81.48 versus 68.78, 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-5.6 Sol or MiniMax M3?
The current coding 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.
Which is better for agentic tasks, GPT-5.6 Sol or MiniMax M3?
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.
Which costs less, GPT-5.6 Sol or MiniMax M3?
For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.0009 on MiniMax M3; repository review costs $0.34 and $0.0186; the cache-heavy agent loop costs $0.5 and $0.03. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Sol or MiniMax M3?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 10, 2026
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