Model comparison
Claude Opus 4.8 vs MiniMax M2.7
Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: Claude Opus 4.8 #3; MiniMax M2.7 unranked
Evidence parity. Claude Opus 4.8 and MiniMax M2.7 share 23 comparable benchmark results. 2 of 8 categories are comparable. 30 results are unique to Claude Opus 4.8; 14 to MiniMax M2.7.
Updated July 12, 2026- Shared results
- 23
- Claude Opus 4.8 only
- 30
- MiniMax M2.7 only
- 14
- Comparable categories
- 2 / 8
Pick Claude Opus 4.8 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 23 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Claude Opus 4.8 is clearly ahead on the provisional aggregate, 85 to 55. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Opus 4.8's sharpest advantage is in agentic, where it averages 80.3 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 74.6% to 57%.
Claude Opus 4.8 is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 20.8x on output cost alone. Claude Opus 4.8 is the reasoning model in the pair, while MiniMax M2.7 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Claude Opus 4.8 gives you the larger context window at 1M, compared with 200K for MiniMax M2.7.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Claude Opus 4.8 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | Claude Opus 4.880.3 | Margin← 23.3 | MiniMax M2.757.0 |
| Coding | Claude Opus 4.876.4 | Margin← 22.0 | MiniMax M2.754.4 |
| Knowledge | Claude Opus 4.862.7 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | Claude Opus 4.853.9 | MarginNo overlap | MiniMax M2.7Not measured |
| Multimodal | Claude Opus 4.877.0 | MarginNo overlap | MiniMax M2.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 74.6%B 57%Winner: Claude Opus 4.8Δ 17.6Terminal-Bench 2.0: Claude Opus 4.8 scored 74.6%; MiniMax M2.7 scored 57%. Claude Opus 4.8 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 69.2%B 56.2%Winner: Claude Opus 4.8Δ 13SWE-bench Pro: Claude Opus 4.8 scored 69.2%; MiniMax M2.7 scored 56.2%. Claude Opus 4.8 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.8 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.8$5 input / $25 output | MiniMax M2.7$0.3 input / $1.2 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.8Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.8Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.81M | MiniMax M2.7200K | Claude Opus 4.8 lists the larger context window. |
Benchmark Deep Dive
AgenticClaude Opus 4.8 wins23 benchmarks
| Benchmark | Claude Opus 4.8 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 74.6% | 57% | Claude Opus 4.8 leads |
| BrowseCompSource | 84.3% | — | Not comparable |
| DeepSearchQASource | 93.1% | — | Not comparable |
| OSWorld-VerifiedSource | 83.4% | — | Not comparable |
| Finance Agent v2Source | 53.9% | — | Not comparable |
| GDPval-AASource | 1600 | 1160 | Claude Opus 4.8 leads |
| MCP AtlasSource | 82.2% | — | Not comparable |
| ToolathlonSource | 59.9% | 46.3% | Claude Opus 4.8 leads |
| Gert LabsSource | 72.97% | 40.40% | Claude Opus 4.8 leads |
| AA Agentic IndexSource | 47.2% | 25.6% | Claude Opus 4.8 leads |
| Tau2-TelecomSource | 94.4% | 84.8% | Claude Opus 4.8 leads |
| GDPval-AASource | 55.0% | 33.0% | Claude Opus 4.8 leads |
| ResearchClawBenchSource | 21.1% | — | Not comparable |
| OSWorld 2.0Source | 20.6% | — | Not comparable |
| AA BriefcaseSource | 1354 | — | Not comparable |
| AA AutomationBenchSource | 48.5% | — | Not comparable |
| AA EnterpriseOps-GymSource | 44.0% | — | Not comparable |
| AA Harvey LABSource | 7.5% | — | Not comparable |
| AA Tau3 BankingSource | 27.6% | — | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
CodingClaude Opus 4.8 wins19 benchmarks
| Benchmark | Claude Opus 4.8 | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 88.6% | — | Not comparable |
| SWE-bench ProSource | 69.2% | 56.2% | Claude Opus 4.8 leads |
| SWE MultilingualSource | 84.4% | 76.5% | Claude Opus 4.8 leads |
| SWE MultimodalSource | 38.4% | — | Not comparable |
| Terminal-Bench 2.0Source | 74.6% | — | Not comparable |
| cursorBench31Source | 58.4% | — | Not comparable |
| cursorBench32Source | 62.3% | — | Not comparable |
| AA Coding IndexSource | 74.3% | 52.6% | Claude Opus 4.8 leads |
| Terminal-Bench HardSource | 58.3% | 39.4% | Claude Opus 4.8 leads |
| AA-SciCodeSource | 53.5% | 47.0% | Claude Opus 4.8 leads |
| FrontierCodeSource | 46.5% | — | Not comparable |
| AA Terminal-Bench 2.1Source | 84.6% | — | Not comparable |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| Multi-SWE BenchSource | — | 52.7% | Not comparable |
| VIBE-ProSource | — | 55.6% | Not comparable |
| NL2RepoSource | — | 39.8% | Not comparable |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning2 benchmarks
Knowledge11 benchmarks
| Benchmark | Claude Opus 4.8 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 93.6% | — | Not comparable |
| GPQA-DSource | 93.6% | 87.0% | Claude Opus 4.8 leads |
| HLESource | 57.9% | — | Not comparable |
| HLE w/o toolsSource | 49.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 55.7% | 38.1% | Claude Opus 4.8 leads |
| AA-GPQA DiamondSource | 92.0% | 87.4% | Claude Opus 4.8 leads |
| AA-HLESource | 45.7% | 28.1% | Claude Opus 4.8 leads |
| AA-Omniscience IndexSource | 27.4% | 0.7% | Claude Opus 4.8 leads |
| AA-Omniscience AccuracySource | 46.6% | 26.1% | Claude Opus 4.8 leads |
| AA-Omniscience Hallucination RateSource | 35.9% | 34.4% | MiniMax M2.7 leads |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math4 benchmarks
Multilingual1 benchmarks
| Benchmark | Claude Opus 4.8 | MiniMax M2.7 | Result |
|---|---|---|---|
| INCLUDESource | 87.6% | — | Not comparable |
Multimodal6 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.8 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 62.2% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Which is better, Claude Opus 4.8 or MiniMax M2.7?
Claude Opus 4.8 is ahead on BenchLM's provisional leaderboard, 85 to 55. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 74.6% and 57%.
Which is better for coding, Claude Opus 4.8 or MiniMax M2.7?
Claude Opus 4.8 has the edge for coding in this comparison, averaging 76.4 versus 54.4. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Claude Opus 4.8 or MiniMax M2.7?
Claude Opus 4.8 has the edge for agentic tasks in this comparison, averaging 80.3 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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