Agentic
Directional only- GPT-5.3 Codex
- 71.4
- MiniMax M2.7
- 57.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
GPT-5.3 Codex vs MiniMax M2.7
Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
20 confirmed releases in the last 30 daysSee provider release alerts
GPT-5.3 Codex has the higher public score estimate, 65.72 versus 63.09, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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-5.3 Codex
GPT-5.3 Codex has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M2.7
MiniMax M2.7 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 M2.7
MiniMax M2.7 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M2.7 does not fit this workload in one request. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 GPT-5.3 Codex only
5 Shared
13 MiniMax M2.7 only
- Shared results
- 5
- GPT-5.3 Codex only
- 3
- MiniMax M2.7 only
- 13
- Like-for-like categories
- 0 / 8
2 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-5.3 Codex
- 67.2
- MiniMax M2.7
- 53.3
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.3 Codex
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.3 Codex
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.3 Codex
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.3 Codex
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.3 Codex
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.3 Codex
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.3 Codex | MiniMax M2.7 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 71.4 | 57.0 | Directional only2 vs 1 rows | Directional only |
| Coding | 67.2 | 53.3 | Directional only3 vs 2 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 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
SWE-Rebench
Coding
SWE-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-5.3 Codex
- $0.00875
- Fits in one request
- MiniMax M2.7
- $0.0009
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.3 Codex
- $0.1295
- Fits in one request
- MiniMax M2.7
- $0.0186
- Fits in one request
MiniMax M2.7 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-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
MiniMax M2.7 does not fit this workload in one request. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.3 CodexMiniMax M2.7
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.3 Codex
MiniMax M2.7
200K
API model ID
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationMiniMax M2.7
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.3 Codex
Not published
MiniMax M2.7
Not published
Documented inputs
GPT-5.3 Codex
text, image
OpenAI model catalogMiniMax M2.7
Not sourced
Documented outputs
GPT-5.3 Codex
MiniMax M2.7
Not sourced
Provider availability
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogMiniMax M2.7
Not sourced
Reasoning profile
GPT-5.3 Codex
Reasoning
MiniMax M2.7
Non-Reasoning
Weight access
GPT-5.3 Codex
Proprietary
MiniMax M2.7
Open Weight
License
GPT-5.3 Codex
Proprietary
MiniMax M2.7
Open Weight
Release date
GPT-5.3 Codex
2026-02-05
MiniMax M2.7
2026-03-18
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-5.3 Codex has the higher public score estimate, 65.72 versus 63.09, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.1295 vs $0.0186. Cache-heavy agent loop: $0.525 vs $0.078.
- Context tradeoff
- GPT-5.3 Codex has the larger documented window (400K).
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 evidence21 rows
Agentic
Terminal-Bench 2.0
GPT-5.3 Codex77.3%SourceMiniMax M2.757%SourceGPT-5.3 Codex leads this result
OSWorld-Verified
GPT-5.3 Codex64.7%SourceMiniMax M2.7—Not directly comparable
Gert Labs
Shared sourceGPT-5.3 Codex57.47%MiniMax M2.740.40%GPT-5.3 Codex leads this result
JobBench
GPT-5.3 Codex33.7%SourceMiniMax M2.7—Not directly comparable
Toolathlon
GPT-5.3 Codex—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
GPT-5.3 Codex—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
GPT-5.3 Codex—MiniMax M2.762.7%SourceNot directly comparable
Claw-Eval
GPT-5.3 Codex—MiniMax M2.748.7%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.3 Codex85%SourceMiniMax M2.7—Not directly comparable
SWE-bench Pro
GPT-5.3 Codex56.8%SourceMiniMax M2.756.2%SourceGPT-5.3 Codex leads this result
SWE-Rebench
GPT-5.3 Codex58.2%SourceMiniMax M2.751.9%SourceGPT-5.3 Codex leads this result
Vibe Code Bench
Shared sourceGPT-5.3 Codex61.77%MiniMax M2.727.04%GPT-5.3 Codex leads this result
SWE-bench Verified*
GPT-5.3 Codex—MiniMax M2.775.4%SourceNot directly comparable
SWE Multilingual
GPT-5.3 Codex—MiniMax M2.776.5%SourceNot directly comparable
Multi-SWE Bench
GPT-5.3 Codex—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
GPT-5.3 Codex—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
GPT-5.3 Codex—MiniMax M2.739.8%SourceNot directly comparable
React Native Evals
GPT-5.3 Codex—MiniMax M2.771.4%SourceNot directly comparable
Knowledge
Math
AIME25 (Arcee)
GPT-5.3 Codex—MiniMax M2.780.0%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.3 Codex or MiniMax M2.7?
GPT-5.3 Codex has the higher public score estimate, 65.72 versus 63.09, 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-5.3 Codex or MiniMax M2.7?
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-5.3 Codex or MiniMax M2.7?
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-5.3 Codex or MiniMax M2.7?
For the stated presets, chat costs $0.00875 on GPT-5.3 Codex and $0.0009 on MiniMax M2.7; repository review costs $0.1295 and $0.0186; the cache-heavy agent loop costs $0.525 and $0.078. MiniMax M2.7 does not fit this workload in one request. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.3 Codex or MiniMax M2.7?
GPT-5.3 Codex has the larger documented context window: 400K, compared with 200K.
Related comparisons
Last updated August 1, 2026
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