Magistral Medium latest vs Mistral Large 2411
Magistral Medium latest (Mistral AI, 40,000-token context) versus Mistral Large 2411 (Mistral AI, 128,000-token context). Magistral Medium latest is cheaper by 13% on a blended token mix. Magistral Medium latest uniquely supports native reasoning mode. 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 — Magistral Medium latest vs Mistral Large 2411
Magistral Medium latest and Mistral Large 2411 target overlapping workloads but differ sharply on economics. Magistral Medium latest runs roughly 13% cheaper on a blended input-plus-output token mix, which translates to approximately $600 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Mistral Large 2411 ships a 128,000-token context window, 3.2x larger than Magistral Medium latest's 40,000 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 40,000 tokens, the extra context on Mistral Large 2411 is insurance you may never use — and Magistral Medium latest may win on other axes.
On capability surface area, the models diverge: Magistral Medium latest supports native reasoning mode 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: magistral-medium-latest
provider: mistral
fallback:
model: mistral-large-2411
provider: mistral
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Magistral Medium latest | Mistral Large 2411 | |
|---|---|---|
| Input price | $2.00/M | $2.00/M |
| Output price | $5.00/M | $6.00/M |
| Context window | 40,000 | 128,000 |
| Max output | 40,000 | 128,000 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | — |
| Prompt caching | — | — |
| Structured output | ✓ | ✓ |
| Pricing verified | Jun 2, 2026 | Jun 2, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
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 | Magistral Medium latest | Mistral Large 2411 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $900 /mo | $960 /mo | $60.00/mo |
| Mid-market 100K requests/day | $9,000 /mo | $9,600 /mo | $600/mo |
| Enterprise 1M requests/day | $90,000 /mo | $96,000 /mo | $6,000/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 — Mistral Large 2411 fits 128,000 tokens versus the other model's 40,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your tasks involve multi-step planning or math-heavy reasoning — Magistral Medium latest ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
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 Magistral Medium latest, switching to Mistral Large 2411 means re-architecting that path (and vice versa).
- • Native reasoning mode
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Structured output (JSON schema)
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 220% when moving from Magistral Medium latest (40,000) to Mistral Large 2411 (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 40,000 on Magistral Medium latest vs 128,000 on Mistral Large 2411. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Magistral Medium latest has capabilities Mistral Large 2411 lacks: Native reasoning mode. Switching to Mistral Large 2411 means re-architecting any flow that depends on these.
How to A/B test Magistral Medium latest vs Mistral Large 2411 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 Magistral Medium latest primary, mirror 20% of traffic to Mistral Large 2411 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 — Magistral Medium latest vs Mistral Large 2411
Which is cheaper, Magistral Medium latest or Mistral Large 2411? ▾
Magistral Medium latest is cheaper by roughly 13% on a blended input + output token mix. Input prices are $2.00/M for Magistral Medium latest versus $2.00/M for Mistral Large 2411; output prices are $5.00/M versus $6.00/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 Magistral Medium latest versus Mistral Large 2411? ▾
Magistral Medium latest supports up to 40,000 tokens of context. Mistral Large 2411 supports up to 128,000 tokens. Mistral Large 2411 has the larger window by a factor of 3.2x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Magistral Medium latest and Mistral Large 2411 both support tool calling? ▾
Yes — both Magistral Medium latest and Mistral Large 2411 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.
When should I choose Magistral Medium latest over Mistral Large 2411? ▾
Your tasks involve multi-step planning or math-heavy reasoning — Magistral Medium latest ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose Mistral Large 2411 over Magistral Medium latest? ▾
Your workload needs long context — Mistral Large 2411 fits 128,000 tokens versus the other model's 40,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
How do I A/B test Magistral Medium latest against Mistral Large 2411 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.