DeepSeek DeepSeek R1.0528 vs Minimaxai Minimax M1.80k
DeepSeek DeepSeek R1.0528 (OpenRouter, 65,336-token context) versus Minimaxai Minimax M1.80k (Novita AI, 1,000,000-token context). DeepSeek DeepSeek R1.0528 is cheaper by 4% on a blended token mix. DeepSeek DeepSeek R1.0528 uniquely supports prompt caching. Minimaxai Minimax M1.80k uniquely supports parallel tool calls. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — DeepSeek DeepSeek R1.0528 vs Minimaxai Minimax M1.80k
DeepSeek DeepSeek R1.0528 and Minimaxai Minimax M1.80k are priced within 4% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
Minimaxai Minimax M1.80k ships a 1,000,000-token context window, 15.3x larger than DeepSeek DeepSeek R1.0528's 65,336 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 65,336 tokens, the extra context on Minimaxai Minimax M1.80k is insurance you may never use — and DeepSeek DeepSeek R1.0528 may win on other axes.
On capability surface area, the models diverge: DeepSeek DeepSeek R1.0528 supports prompt caching where the other does not; Minimaxai Minimax M1.80k supports parallel tool calls where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: deepseek-deepseek-r1-0528
provider: openrouter
fallback:
model: minimaxai-minimax-m1-80k
provider: novita-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek DeepSeek R1.0528 | Minimaxai Minimax M1.80k | |
|---|---|---|
| Input price | $0.500/M | $0.550/M |
| Output price | $2.15/M | $2.20/M |
| Context window | 65,336 | 1,000,000 |
| Max output | 8,192 | 40,000 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | — |
| Structured output | — | — |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | DeepSeek DeepSeek R1.0528 | Minimaxai Minimax M1.80k | Delta |
|---|---|---|---|
| Startup 10K requests/day | $279 /mo | $297 /mo | $18.00/mo |
| Mid-market 100K requests/day | $2,790 /mo | $2,970 /mo | $180/mo |
| Enterprise 1M requests/day | $27,900 /mo | $29,700 /mo | $1,800/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — Minimaxai Minimax M1.80k fits 1,000,000 tokens versus the other model's 65,336, enough headroom for full books, large codebases, or 100+ page documents in one shot.
You re-send the same large system prompt across requests — DeepSeek DeepSeek R1.0528 supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only DeepSeek DeepSeek R1.0528, switching to Minimaxai Minimax M1.80k means re-architecting that path (and vice versa).
- • Prompt caching
- • Parallel tool calls
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 1431% when moving from DeepSeek DeepSeek R1.0528 (65,336) to Minimaxai Minimax M1.80k (1,000,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on DeepSeek DeepSeek R1.0528 vs 40,000 on Minimaxai Minimax M1.80k. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- DeepSeek DeepSeek R1.0528 has capabilities Minimaxai Minimax M1.80k lacks: Prompt caching. Switching to Minimaxai Minimax M1.80k means re-architecting any flow that depends on these.
- Minimaxai Minimax M1.80k has capabilities DeepSeek DeepSeek R1.0528 lacks: Parallel tool calls. Worth wiring through the agent design before commit.
- Provider changes from OpenRouter to Novita AI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test DeepSeek DeepSeek R1.0528 vs Minimaxai Minimax M1.80k in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark DeepSeek DeepSeek R1.0528 primary, mirror 20% of traffic to Minimaxai Minimax M1.80k in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — DeepSeek DeepSeek R1.0528 vs Minimaxai Minimax M1.80k
Which is cheaper, DeepSeek DeepSeek R1.0528 or Minimaxai Minimax M1.80k? ▾
DeepSeek DeepSeek R1.0528 is cheaper by roughly 4% on a blended input + output token mix. Input prices are $0.500/M for DeepSeek DeepSeek R1.0528 versus $0.550/M for Minimaxai Minimax M1.80k; output prices are $2.15/M versus $2.20/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of DeepSeek DeepSeek R1.0528 versus Minimaxai Minimax M1.80k? ▾
DeepSeek DeepSeek R1.0528 supports up to 65,336 tokens of context. Minimaxai Minimax M1.80k supports up to 1,000,000 tokens. Minimaxai Minimax M1.80k has the larger window by a factor of 15.3x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do DeepSeek DeepSeek R1.0528 and Minimaxai Minimax M1.80k both support tool calling? ▾
Yes — both DeepSeek DeepSeek R1.0528 and Minimaxai Minimax M1.80k support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
DeepSeek DeepSeek R1.0528 supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, DeepSeek DeepSeek R1.0528 gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose DeepSeek DeepSeek R1.0528 over Minimaxai Minimax M1.80k? ▾
You re-send the same large system prompt across requests — DeepSeek DeepSeek R1.0528 supports prompt caching, cutting input cost on repeat hits.
When should I choose Minimaxai Minimax M1.80k over DeepSeek DeepSeek R1.0528? ▾
Your workload needs long context — Minimaxai Minimax M1.80k fits 1,000,000 tokens versus the other model's 65,336, enough headroom for full books, large codebases, or 100+ page documents in one shot.
How do I A/B test DeepSeek DeepSeek R1.0528 against Minimaxai Minimax M1.80k in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.