Mistral Medium 3.5 vs Pixtral Large latest
Mistral Medium 3.5 (Mistral AI, 262,144-token context) versus Pixtral Large latest (Mistral AI, 128,000-token context). Pixtral Large latest is cheaper by 11% on a blended token mix. Mistral Medium 3.5 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 — Mistral Medium 3.5 vs Pixtral Large latest
Mistral Medium 3.5 and Pixtral Large latest target overlapping workloads but differ sharply on economics. Pixtral Large latest runs roughly 11% 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 Medium 3.5 ships a 262,144-token context window, 2.0x larger than Pixtral Large latest's 128,000 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 128,000 tokens, the extra context on Mistral Medium 3.5 is insurance you may never use — and Pixtral Large latest may win on other axes.
On capability surface area, the models diverge: Mistral Medium 3.5 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: mistral-medium-3-5
provider: mistral
fallback:
model: pixtral-large-latest
provider: mistral
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Mistral Medium 3.5 | Pixtral Large latest | |
|---|---|---|
| Input price | $1.50/M | $2.00/M |
| Output price | $7.50/M | $6.00/M |
| Context window | 262,144 | 128,000 |
| Max output | 262,144 | 128,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | — |
| Prompt caching | — | — |
| Structured output | ✓ | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 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 | Mistral Medium 3.5 | Pixtral Large latest | 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 Medium 3.5 fits 262,144 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your tasks involve multi-step planning or math-heavy reasoning — Mistral Medium 3.5 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 Mistral Medium 3.5, switching to Pixtral Large latest means re-architecting that path (and vice versa).
- • Native reasoning mode
Capabilities both share (4)
- ✓ Function calling
- ✓ Vision input
- ✓ 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 down 51% when moving from Mistral Medium 3.5 (262,144) to Pixtral Large latest (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 262,144 on Mistral Medium 3.5 vs 128,000 on Pixtral Large latest. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Mistral Medium 3.5 has capabilities Pixtral Large latest lacks: Native reasoning mode. Switching to Pixtral Large latest means re-architecting any flow that depends on these.
How to A/B test Mistral Medium 3.5 vs Pixtral Large latest 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 Mistral Medium 3.5 primary, mirror 20% of traffic to Pixtral Large latest 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 — Mistral Medium 3.5 vs Pixtral Large latest
Which is cheaper, Mistral Medium 3.5 or Pixtral Large latest? ▾
Pixtral Large latest is cheaper by roughly 11% on a blended input + output token mix. Input prices are $1.50/M for Mistral Medium 3.5 versus $2.00/M for Pixtral Large latest; output prices are $7.50/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 Mistral Medium 3.5 versus Pixtral Large latest? ▾
Mistral Medium 3.5 supports up to 262,144 tokens of context. Pixtral Large latest supports up to 128,000 tokens. Mistral Medium 3.5 has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Mistral Medium 3.5 and Pixtral Large latest both support tool calling? ▾
Yes — both Mistral Medium 3.5 and Pixtral Large latest 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 Mistral Medium 3.5 over Pixtral Large latest? ▾
Your workload needs long context — Mistral Medium 3.5 fits 262,144 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your tasks involve multi-step planning or math-heavy reasoning — Mistral Medium 3.5 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose Pixtral Large latest over Mistral Medium 3.5? ▾
On the data this page surfaces, Pixtral Large latest is the right pick when Mistral Medium 3.5's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test Mistral Medium 3.5 against Pixtral Large latest 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.