Coding
Like-for-like- GPT-5.6 Terra
- 63.4
- MiMo-V2.5-Pro
- 57.2
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Terra leads
Model comparison
GPT-5.6 Terra vs MiMo-V2.5-Pro
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Terra has the higher public score estimate, 72.29 versus 69.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Terra
GPT-5.6 Terra leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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
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.
19 GPT-5.6 Terra only
3 Shared
5 MiMo-V2.5-Pro only
- Shared results
- 3
- GPT-5.6 Terra only
- 19
- MiMo-V2.5-Pro only
- 5
- Like-for-like categories
- 1 / 8
1 category uses 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.
Agentic
Directional only- GPT-5.6 Terra
- 87.4
- MiMo-V2.5-Pro
- 68.4
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Terra
- 83.9
- MiMo-V2.5-Pro
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Terra
- 92.9
- MiMo-V2.5-Pro
- 48.0
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Terra
- 80.8
- MiMo-V2.5-Pro
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Terra
- Not measured
- MiMo-V2.5-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Terra
- 80.7
- MiMo-V2.5-Pro
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Terra
- Not measured
- MiMo-V2.5-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Terra | MiMo-V2.5-Pro | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 63.4 | 57.2 | Like-for-like1 vs 1 rows | GPT-5.6 Terra leads |
| Agentic | 87.4 | 68.4 | Directional only2 vs 1 rows | Directional only |
| Reasoning | 83.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 92.9 | 48.0 | Not comparable1 vs 1 rows | Not comparable |
| Math | 80.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 80.7 | Not measured | Not comparable1 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
GPT-5.6 Terra: 87.4%MiMo-V2.5-Pro: 68.4%Normalized gap 19.0GPT-5.6 Terra source MiMo-V2.5-Pro sourceSWE-bench Pro
Coding
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 Terra
- $0.008
- 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 Terra
- $0.136
- 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 Terra
- $0.2
- 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-5.6 TerraMiMo-V2.5-Pro
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Terra
1.05M
OpenAI model catalogMiMo-V2.5-Pro
1M
API model ID
GPT-5.6 Terra
gpt-5.6-terra
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 Terra
$0.2 per 1M cached input tokens
OpenAI pricingMiMo-V2.5-Pro
No comparable hosted API rate
Documented inputs
GPT-5.6 Terra
text, image
OpenAI model catalogMiMo-V2.5-Pro
Not sourced
Documented outputs
GPT-5.6 Terra
MiMo-V2.5-Pro
Not sourced
Provider availability
GPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogMiMo-V2.5-Pro
Not sourced
Reasoning profile
GPT-5.6 Terra
Reasoning
MiMo-V2.5-Pro
Reasoning
Weight access
GPT-5.6 Terra
Proprietary
MiMo-V2.5-Pro
Proprietary
License
GPT-5.6 Terra
Proprietary
MiMo-V2.5-Pro
Proprietary
Release date
GPT-5.6 Terra
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 Terra has the higher public score estimate, 72.29 versus 69.2, 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 Terra 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 evidence27 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Terra87.4%SourceMiMo-V2.5-Pro68.4%SourceGPT-5.6 Terra leads this result
BrowseComp
GPT-5.6 Terra87.5%SourceMiMo-V2.5-Pro—Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%SourceMiMo-V2.5-Pro—Not directly comparable
CyberGym
GPT-5.6 Terra81.8%SourceMiMo-V2.5-Pro—Not directly comparable
ExploitGym
GPT-5.6 Terra23.2%SourceMiMo-V2.5-Pro—Not directly comparable
Toolathlon
GPT-5.6 Terra53.1%SourceMiMo-V2.5-Pro—Not directly comparable
Claw-Eval
GPT-5.6 Terra—MiMo-V2.5-Pro63.8%SourceNot directly comparable
τ³-bench results
GPT-5.6 Terra—MiMo-V2.5-Pro72.9%SourceNot directly comparable
Gert Labs
GPT-5.6 Terra—MiMo-V2.5-Pro62.70%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Terra63.4%SourceMiMo-V2.5-Pro57.2%SourceGPT-5.6 Terra leads this result
Terminal-Bench 2.0
GPT-5.6 Terra87.4%SourceMiMo-V2.5-Pro68.4%SourceGPT-5.6 Terra leads this result
deepSwe
GPT-5.6 Terra69.6%SourceMiMo-V2.5-Pro—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%SourceMiMo-V2.5-Pro—Not directly comparable
cursorBench32
GPT-5.6 Terra64.9%SourceMiMo-V2.5-Pro—Not directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Terra92.9%SourceMiMo-V2.5-Pro—Not directly comparable
GPQA-D
GPT-5.6 Terra92.9%SourceMiMo-V2.5-Pro—Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%SourceMiMo-V2.5-Pro—Not directly comparable
HealthBench Hard
GPT-5.6 Terra32.7%SourceMiMo-V2.5-Pro—Not directly comparable
HLE
GPT-5.6 Terra—MiMo-V2.5-Pro48%SourceNot directly comparable
HLE w/o tools
GPT-5.6 Terra—MiMo-V2.5-Pro34%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.6 Terra or MiMo-V2.5-Pro?
GPT-5.6 Terra has the higher public score estimate, 72.29 versus 69.2, 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 Terra or MiMo-V2.5-Pro?
GPT-5.6 Terra leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, GPT-5.6 Terra or MiMo-V2.5-Pro?
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 Terra 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 Terra or MiMo-V2.5-Pro?
GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 10, 2026
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