Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Luna
GPT-5.6 Luna leads on the public coding lane, 64.5 to 45.7, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
GPT-5.6 Luna vs MiMo-V2.5-Pro
Updated September 24, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.6 Luna has the higher public score estimate, 65.6 versus 52.03, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- GPT-5.6 Luna only
- 24
- MiMo-V2.5-Pro only
- 8
- Like-for-like categories
- 3 / 8
Supported: GPT-5.6 Luna and MiMo-V2.5-ProHow the comparison works
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Luna
GPT-5.6 Luna leads on the public agentic lane, 55.3 to 37.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
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.
64.5GPT-5.6 Luna45.7MiMo-V2.5-Pro
Like-for-like · BenchAlign v5.7
GPT-5.6 Luna leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
24 GPT-5.6 Luna only
5 Shared
8 MiMo-V2.5-Pro only
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.
SWE-bench ProCoding
Normalized gap 5.5GPT-5.6 Luna:62.7%MiMo-V2.5-Pro:57.2%MMLU-Pro (Vals)Knowledge
Normalized gap 1.4GPT-5.6 Luna:86.0%MiMo-V2.5-Pro:84.6%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Agentic
Like-for-like- GPT-5.6 Luna
- 55.3
- Supported · #28/105
- MiMo-V2.5-Pro
- 37.5
- Supported · #48/105
- Basis
- BenchAlign v5.7 lane · 9 vs 5 public rows
- Reading
- GPT-5.6 Luna leads · intervals overlap
Coding
Like-for-like- GPT-5.6 Luna
- 64.5
- Supported · #9/135
- MiMo-V2.5-Pro
- 45.7
- Supported · #46/135
- Basis
- BenchAlign v5.7 lane · 7 vs 4 public rows
- Reading
- GPT-5.6 Luna leads
Knowledge
Like-for-like- GPT-5.6 Luna
- 64.6
- Supported · #22/158
- MiMo-V2.5-Pro
- 49.8
- Supported · #57/158
- Basis
- BenchAlign v5.7 lane · 6 vs 4 public rows
- Reading
- GPT-5.6 Luna leads · intervals overlap
Reasoning
Not comparable- GPT-5.6 Luna
- 54.7
- #18/19
- MiMo-V2.5-Pro
- 76.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Luna
- 67.1
- #22/50
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not ranked
- MiMo-V2.5-Pro
- 92.4
- #6/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.3Supported · #28/105 | 37.5Supported · #48/105 | Like-for-likeBenchAlign v5.7 lane · 9 vs 5 public rows | GPT-5.6 Luna leads · intervals overlap |
| Coding | 64.5Supported · #9/135 | 45.7Supported · #46/135 | Like-for-likeBenchAlign v5.7 lane · 7 vs 4 public rows | GPT-5.6 Luna leads |
| Knowledge | 64.6Supported · #22/158 | 49.8Supported · #57/158 | Like-for-likeBenchAlign v5.7 lane · 6 vs 4 public rows | GPT-5.6 Luna leads · intervals overlap |
| Reasoning | 54.7#18/19 | 76.8Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 67.1#22/50 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.4#6/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 94.2Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
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 Luna
- $0.0008
- Fits in one request
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
MiMo-V2.5-Pro has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.0136
- Fits in one request
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
MiMo-V2.5-Pro has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.02
- Fits in one request
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MiMo-V2.5-Pro has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 LunaMiMo-V2.5-Pro
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogMiMo-V2.5-Pro
1M
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogMiMo-V2.5-Pro
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 Luna
$0.02 per 1M cached input tokens
OpenAI pricingMiMo-V2.5-Pro
No comparable hosted API rate
Documented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogMiMo-V2.5-Pro
Not sourced
Documented outputs
GPT-5.6 Luna
MiMo-V2.5-Pro
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogMiMo-V2.5-Pro
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
MiMo-V2.5-Pro
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
MiMo-V2.5-Pro
Proprietary
License
GPT-5.6 Luna
Proprietary
MiMo-V2.5-Pro
Proprietary
Release date
GPT-5.6 Luna
2026-07-09
MiMo-V2.5-Pro
2026-04-22
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 Luna has the higher public score estimate, 65.6 versus 52.03, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.6 Luna or MiMo-V2.5-Pro?
GPT-5.6 Luna has the higher public score estimate, 65.6 versus 52.03, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.6 Luna or MiMo-V2.5-Pro?
GPT-5.6 Luna leads the public coding lane, 64.5 to 45.7, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.6 Luna or MiMo-V2.5-Pro?
GPT-5.6 Luna leads the public agentic tasks lane, 55.3 to 37.5, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GPT-5.6 Luna or MiMo-V2.5-Pro?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-5.6 Luna or MiMo-V2.5-Pro?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence37 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Luna14.3%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Luna84.7%SourceMiMo-V2.5-Pro—Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%SourceMiMo-V2.5-Pro—Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%SourceMiMo-V2.5-Pro—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceMiMo-V2.5-Pro—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceMiMo-V2.5-Pro—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna79.0%SourceMiMo-V2.5-Pro57.3%SourceGPT-5.6 Luna leads this result
ApprenticeBench
GPT-5.6 Luna7%SourceMiMo-V2.5-Pro—Not directly comparable
Claw-Eval
GPT-5.6 Luna—MiMo-V2.5-Pro63.8%SourceNot directly comparable
τ³-bench results
GPT-5.6 Luna—MiMo-V2.5-Pro72.9%SourceNot directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna—MiMo-V2.5-Pro68.4%SourceNot directly comparable
Gert Labs
GPT-5.6 Luna—MiMo-V2.5-Pro62.70%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceMiMo-V2.5-Pro57.2%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.1
GPT-5.6 Luna84.7%SourceMiMo-V2.5-Pro—Not directly comparable
DeepSWE
GPT-5.6 Luna67.2%SourceMiMo-V2.5-Pro—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceMiMo-V2.5-Pro—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceMiMo-V2.5-Pro—Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%SourceMiMo-V2.5-Pro—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna93.0%SourceMiMo-V2.5-Pro74.0%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.0
GPT-5.6 Luna—MiMo-V2.5-Pro68.4%SourceNot directly comparable
LiveCodeBench (Vals)
GPT-5.6 Luna—MiMo-V2.5-Pro81.4%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceMiMo-V2.5-Pro—Not directly comparable
GPQA-D
GPT-5.6 Luna92.3%SourceMiMo-V2.5-Pro—Not directly comparable
HealthBench Professional
GPT-5.6 Luna55.7%SourceMiMo-V2.5-Pro—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceMiMo-V2.5-Pro—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna91.7%SourceMiMo-V2.5-Pro82.6%SourceGPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GPT-5.6 Luna86.0%SourceMiMo-V2.5-Pro84.6%SourceGPT-5.6 Luna leads this result
HLE
GPT-5.6 Luna—MiMo-V2.5-Pro48%SourceNot directly comparable
HLE w/o tools
GPT-5.6 Luna—MiMo-V2.5-Pro34%SourceNot directly comparable
Math
37 public results · 5 shared
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Last updated September 24, 2026