Agentic
Like-for-like- GPT-5.5
- 63.9
- Supported · #15/151
- MiMo-V2.5-Pro
- 40.8
- Supported · #118/151
- Basis
- BenchAlign lane · 13 vs 5 public rows
- Reading
- GPT-5.5 leads
GPT-5.5 vs MiMo-V2.5-Pro
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score estimate, 73.27 versus 65.41, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
11 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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-5.5
GPT-5.5 leads on the public agentic lane, 63.9 to 40.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.5-Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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-rates
Cache-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-rates
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
27 GPT-5.5 only
11 Shared
2 MiMo-V2.5-Pro only
- Shared results
- 11
- GPT-5.5 only
- 27
- MiMo-V2.5-Pro only
- 2
- Like-for-like categories
- 2 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Knowledge
Like-for-like- GPT-5.5
- 73.3
- Supported · #7/181
- MiMo-V2.5-Pro
- 55.5
- Supported · #58/181
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Coding
Directional only- GPT-5.5
- 67.7
- Supported · #8/183
- MiMo-V2.5-Pro
- 57.1
- Estimated · #36/183
- Basis
- BenchAlign lane · 9 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 92.9
- #7/120
- MiMo-V2.5-Pro
- 93.5
- #6/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 63.5
- #15/22
- MiMo-V2.5-Pro
- 76.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5
- 71.3
- #19/48
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 63.9Supported · #15/151 | 40.8Supported · #118/151 | Like-for-likeBenchAlign lane · 13 vs 5 public rows | GPT-5.5 leads |
| Knowledge | 73.3Supported · #7/181 | 55.5Supported · #58/181 | Like-for-likeBenchAlign lane · 6 vs 4 public rows | GPT-5.5 leads · intervals overlap |
| Coding | 67.7Supported · #8/183 | 57.1Estimated · #36/183 | Directional onlyBenchAlign lane · 9 vs 4 public rows | Directional only |
| Instruction following | 92.9#7/120 | 93.5#6/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 63.5#15/22 | 76.9Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 rankable rows | 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 |
| Multimodal | 71.3#19/48 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
HLE w/o tools
Knowledge
HLE
Knowledge
LiveCodeBench (Vals)
Coding
MMLU-Pro (Vals)
Knowledge
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-5.5
- $0.02
- 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-5.5
- $0.34
- 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-5.5
- $0.5
- 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.5MiMo-V2.5-Pro
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
MiMo-V2.5-Pro
1M
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingMiMo-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-5.5
$0.5 per 1M cached input tokens
OpenAI pricingMiMo-V2.5-Pro
No comparable hosted API rate
Documented inputs
GPT-5.5
Not sourced
MiMo-V2.5-Pro
Not sourced
Documented outputs
GPT-5.5
Not sourced
MiMo-V2.5-Pro
Not sourced
Provider availability
GPT-5.5
Not sourced
MiMo-V2.5-Pro
Not sourced
Reasoning profile
GPT-5.5
Reasoning
MiMo-V2.5-Pro
Reasoning
Weight access
GPT-5.5
Proprietary
MiMo-V2.5-Pro
Proprietary
License
GPT-5.5
Proprietary
MiMo-V2.5-Pro
Proprietary
Release date
GPT-5.5
2026-04-23
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-5.5 has the higher public score estimate, 73.27 versus 65.41, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
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 evidence40 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceMiMo-V2.5-Pro68.4%SourceGPT-5.5 leads this result
CyberGym
GPT-5.581.8%SourceMiMo-V2.5-Pro—Not directly comparable
BrowseComp
GPT-5.584.4%SourceMiMo-V2.5-Pro—Not directly comparable
OSWorld-Verified
GPT-5.578.7%SourceMiMo-V2.5-Pro—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceMiMo-V2.5-Pro—Not directly comparable
Toolathlon
GPT-5.555.6%SourceMiMo-V2.5-Pro—Not directly comparable
τ²-bench results
GPT-5.598%SourceMiMo-V2.5-Pro—Not directly comparable
Gert Labs
Shared sourceGPT-5.572.93%MiMo-V2.5-Pro62.70%GPT-5.5 leads this result
ResearchClawBench
GPT-5.517.0%SourceMiMo-V2.5-Pro—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceMiMo-V2.5-Pro—Not directly comparable
JobBench
GPT-5.542.7%SourceMiMo-V2.5-Pro—Not directly comparable
ExploitGym
GPT-5.513.4%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceMiMo-V2.5-Pro57.3%SourceGPT-5.5 leads this result
Claw-Eval
GPT-5.5—MiMo-V2.5-Pro63.8%SourceNot directly comparable
τ³-bench results
GPT-5.5—MiMo-V2.5-Pro72.9%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceMiMo-V2.5-Pro57.2%SourceGPT-5.5 leads this result
Terminal-Bench 2.0
GPT-5.582.0%SourceMiMo-V2.5-Pro68.4%SourceGPT-5.5 leads this result
Vibe Code Bench
GPT-5.569.85%SourceMiMo-V2.5-Pro—Not directly comparable
React Native Evals
GPT-5.584.7%SourceMiMo-V2.5-Pro—Not directly comparable
cursorBench31
GPT-5.559.2%SourceMiMo-V2.5-Pro—Not directly comparable
cursorBench32
GPT-5.558.4%SourceMiMo-V2.5-Pro—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceMiMo-V2.5-Pro—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceMiMo-V2.5-Pro81.4%SourceGPT-5.5 leads this result
SWE-bench (Vals)
GPT-5.582.6%SourceMiMo-V2.5-Pro74.0%SourceGPT-5.5 leads this result
Reasoning
Knowledge
GPQA
GPT-5.593.6%SourceMiMo-V2.5-Pro—Not directly comparable
GPQA-D
GPT-5.593.6%SourceMiMo-V2.5-Pro—Not directly comparable
HLE
GPT-5.552.2%SourceMiMo-V2.5-Pro48%SourceGPT-5.5 leads this result
HLE w/o tools
GPT-5.541.4%SourceMiMo-V2.5-Pro34%SourceGPT-5.5 leads this result
GPQA Diamond (Vals)
GPT-5.593.2%SourceMiMo-V2.5-Pro82.6%SourceGPT-5.5 leads this result
MMLU-Pro (Vals)
GPT-5.588.1%SourceMiMo-V2.5-Pro84.6%SourceGPT-5.5 leads this result
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.5 or MiMo-V2.5-Pro?
GPT-5.5 has the higher public score estimate, 73.27 versus 65.41, 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-5.5 or MiMo-V2.5-Pro?
GPT-5.5 scores higher for coding on the public lane, 67.7 to 57.1. MiMo-V2.5-Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.5 or MiMo-V2.5-Pro?
GPT-5.5 leads the public agentic tasks lane, 63.9 to 40.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GPT-5.5 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-5.5 or MiMo-V2.5-Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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